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"visibility": null, "width": null } }, "d8f5cb5ee8b348dcaec5786fb2e4392b": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } }, "accelerator": "GPU" }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "view-in-github", "colab_type": "text" }, "source": [ "\"Open" ] }, { "cell_type": "code", "source": [ "!pip install datasets umap-learn &> /dev/null" ], "metadata": { "id": "NYH_1HU-5BFA" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# Learning sequence-to-function relationship using language models" ], "metadata": { "id": "w_nOAEhm2isM" } }, { "cell_type": "markdown", "source": [ "Protein engineering is like jazzing up Mother Nature's enzymes – we tweak them for stability, catalytic action, and substrate specificity. To cook up these cool proteins, we've to crack the code linking a protein's sequence to its superpowers. Protein sequence-to-function mapping is a wild ride – think thousands of molecular tango moves, coupled over multiple lengths and timescales.\n", "\n", "Enter deep mutational scanning (DMS) experiments – these efficiently map tens of thousands of protein variations to corresponding phenotypic changes. Supervised machine learning steps in, learning the ropes of mapping specific protein properties directly from real-life sequence–function showdowns. The learned models like these have been shown to craft brand-new proteins that outshine anything nature cooked up. It's like having an AI protein designer in the lab, pushing the boundaries of what's naturally possible.\n", "\n", "\n", "In this and following notebook, we will dive into training a variant predictor i.e. predicting the biological activity of mutations of a protein, using embeddings from Protein Language Model (PLM).\n", "\n", "**Goals**\n", "\n", "- Prepare the DMS data for machine learning\n", "- Obtain an embedding (fixed-dimensional vector representation) for each mutated sequence from PLM.\n", "- Split the data into train and test set\n", "- Train a regression model in sklearn that can predict the \"fitness\" score given the embedding." ], "metadata": { "id": "6DkGQFk1cfkp" } }, { "cell_type": "markdown", "source": [ "## The dataset" ], "metadata": { "id": "VWFUJYIEim8Q" } }, { "cell_type": "markdown", "source": [ "To build our protein sequence-to-function predictor we will use the [DMS data](https://raw.githubusercontent.com/churchlab/aav_rep_scan/master/analysis/selection_values/rep7868_selection_values_barcode.csv) from [rep mutagenesis scan paper published in 2023 from George M. Church lab](https://doi.org/10.7554/eLife.87730.1). This paper examines the mutational landscape of Rep protein by assaying the effects of all 39,297 possible single codon mutations to the adeno-associated virus 2(AAV2) rep gene on AAV2 production. The AAV Rep proteins, indispensable for genome replication and packaging, present a favorable engineering target for improving of recombinant adeno-associated virus (rAAV) production for therapeutic purposes.\n", "\n", "The data consists of the mutated Rep78 sequence, where a single aminoacid residue is mutated (swapped with another amino acid) and the target value that describes the fitness of the mutation.\n", "\n" ], "metadata": { "id": "hSJydoxwjRfO" } }, { "cell_type": "code", "source": [ "# Import libraries\n", "from pathlib import Path\n", "import os\n", "import pickle\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import re\n", "from functools import partial\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "sns.set_context(context=\"paper\", font_scale=1.5)" ], "metadata": { "id": "12JOCFU7VjXi" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Wild type sequence of Rep78 from AAV2\n", "rep78_seq = 'MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLNLIEQAPLTVAEKLQRDFLTEWRRVSKAPEALFFVQFEKGESYFHMHVLVETTGVKSMVLGRFLSQIREKLIQRIYRGIEPTLPNWFAVTKTRNGAGGGNKVVDECYIPNYLLPKTQPELQWAWTNMEQYLSACLNLTERKRLVAQHLTHVSQTQEQNKENQNPNSDAPVIRSKTSARYMELVGWLVDKGITSEKQWIQEDQASYISFNAASNSRSQIKAALDNAGKIMSLTKTAPDYLVGQQPVEDISSNRIYKILELNGYDPQYAASVFLGWATKKFGKRNTIWLFGPATTGKTNIAEAIAHTVPFYGCVNWTNENFPFNDCVDKMVIWWEEGKMTAKVVESAKAILGGSKVRVDQKCKSSAQIDPTPVIVTSNTNMCAVIDGNSTTFEHQQPLQDRMFKFELTRRLDHDFGKVTKQEVKDFFRWAKDHVVEVEHEFYVKKGGAKKRPAPSDADISEPKRVRESVAQPSTSDAEASINYADRYQNKCSRHVGMNLMLFPCRQCERMNQNSNICFTHGQKDCLECFPVSESQPVSVVKKAYQKLCYIHHIMGKVPDACTACDLVNVDLDDCIFEQ'\n", "len(rep78_seq)" ], "metadata": { "id": "Hcfo_SsB35mr", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "f6fd1d36-ac0f-4650-c8bb-e7d33f96e8a2" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "621" ] }, "metadata": {}, "execution_count": 3 } ] }, { "cell_type": "markdown", "source": [ "In the paper rep open reading frame with a M225G mutation was introduced to prevent expression of the smaller Rep proteins. The rep seuqence with this substitution was considered as wild-type sequence." ], "metadata": { "id": "-B-NY3ZmmFuU" } }, { "cell_type": "code", "source": [ "# change M225G. This will be considered wild type sequence from here on. Python string is zero-indexed\n", "wt_seq = rep78_seq[:224] + 'G' + rep78_seq[225:]\n", "len(wt_seq) # ensure length remains same after substitution" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "txlZFjLnmQwf", "outputId": "eef5dae1-45bd-4a41-af9e-51d9d5643486" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "621" ] }, "metadata": {}, "execution_count": 4 } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "PxFXDw1SWsH5" }, "outputs": [], "source": [ "# Link to csv file with fitness data from paper\n", "rep7868_selection_values_barcode = 'https://raw.githubusercontent.com/churchlab/aav_rep_scan/master/analysis/selection_values/rep7868_selection_values_barcode.csv'" ] }, { "cell_type": "code", "source": [ "# Read the fitness data into a pandas dataframe\n", "# a, b = fitness values for transfection duplicates\n", "fitness_data = pd.read_csv(rep7868_selection_values_barcode)\n", "fitness_data" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "wr5ypfSI37MU", "outputId": "133b7776-b6df-4cf6-dcb2-5b088e5a2dc8" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " abs_pos aa codon lib_type barcode is_wt a \\\n", "0 1 * TAA del GTGAGACTGACACACTGTCT 0 0.471768 \n", "1 1 * TAA del TCAGACTCTGACTGAGACAG 0 0.928537 \n", "2 1 * TAA sub ACTGAGAGAGTGACAGACTG 0 0.609253 \n", "3 1 * TAA sub TCTGAGTCTCACACTCACAG 0 0.497817 \n", "4 1 * TAG sub GACACTCAGAGAGACTGAGT 0 0.442826 \n", "... ... .. ... ... ... ... ... \n", "80959 622 W TGG sub TCTGACACACTGACACTGAC 0 0.729599 \n", "80960 622 Y TAC sub CTGAGTGTCAGACTGACTCA 0 0.902470 \n", "80961 622 Y TAC sub TCAGTGTGTGACAGACTGTC 0 0.714219 \n", "80962 622 Y TAT sub AGACAGACACAGTGTCTCTG 0 0.000000 \n", "80963 622 Y TAT sub TGAGACTGTCACTGTCTCTC 0 0.796220 \n", "\n", " b \n", "0 0.289412 \n", "1 0.656928 \n", "2 0.231558 \n", "3 0.493451 \n", "4 0.194076 \n", "... ... \n", "80959 0.787785 \n", "80960 1.056134 \n", "80961 0.628245 \n", "80962 0.000000 \n", "80963 0.435428 \n", "\n", "[80964 rows x 8 columns]" ], "text/html": [ "\n", "
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abs_posaacodonlib_typebarcodeis_wtab
01*TAAdelGTGAGACTGACACACTGTCT00.4717680.289412
11*TAAdelTCAGACTCTGACTGAGACAG00.9285370.656928
21*TAAsubACTGAGAGAGTGACAGACTG00.6092530.231558
31*TAAsubTCTGAGTCTCACACTCACAG00.4978170.493451
41*TAGsubGACACTCAGAGAGACTGAGT00.4428260.194076
...........................
80959622WTGGsubTCTGACACACTGACACTGAC00.7295990.787785
80960622YTACsubCTGAGTGTCAGACTGACTCA00.9024701.056134
80961622YTACsubTCAGTGTGTGACAGACTGTC00.7142190.628245
80962622YTATsubAGACAGACACAGTGTCTCTG00.0000000.000000
80963622YTATsubTGAGACTGTCACTGTCTCTC00.7962200.435428
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80964 rows × 8 columns

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\n" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "markdown", "source": [ "Brief description of the columns in the dataset:\n", "\n", "**`abs_pos`**: Indicates amino acid position where the mutation has been made.\n", "\n", "**`aa`**: Represents the substituted amino acid at a given position. For example, * represents substitution with a stop codon, and Y represents substitution with a codon coding for amino acid Y.\n", "\n", "**`codon`**: A nucleotide triplet that codes for a given amino acid. For instance, TAA codes for *, and TAC codes for amino acid Y.\n", "\n", "**`lib_type`**: Indicates whether a mutation is a substitution (sub) or a deletion (del).\n", "\n", "**`barcode`**: Represents each unique variant or replicate. Fitness is essentially determined as the normalized frequency of barcode counts.\n", "\n", "**`is_wt`**: Indicates whether the substitution is wildtype or not. A value of 0 represents not wildtype.\n", "\n", "**`a`**: Reflects the fitness value for first biological replicate.\n", "\n", "**`b`**: Reflects the fitness value for second biological replicate." ], "metadata": { "id": "ZLjMBGV6FREj" } }, { "cell_type": "markdown", "source": [ "## Data processing" ], "metadata": { "id": "bf4ah9yz6Nkq" } }, { "cell_type": "markdown", "source": [ "In this segment, we will dive into Exploratory Data Analysis (EDA) and transform the data into a format amenable for supervised machine learning. Our goal is to create a pair of lists: `sequences` and `fitness`. `sequences` will be a list of protein sequences. `fitness` will be a list of score for each protein sequence. 'fitness` refers to quantitative value of property of protein sequence related to function that the experimenter is interested in optimizing." ], "metadata": { "id": "aE4jgI7p5GPp" } }, { "cell_type": "code", "source": [ "# Remove rows containing stop codon (truncated proteins smaller than 621 AA)\n", "# And remove rows with data for 622 aminoacid position (rep78 is only 621 AA long)\n", "fitness_data_sub = fitness_data.loc[-((fitness_data['aa'] == '*')|(fitness_data['abs_pos'] == 622))].copy()\n", "fitness_data_sub" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "38Y5knN54KGa", "outputId": "83ba963a-8550-4b7c-a6c5-7f587f918b89" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " abs_pos aa codon lib_type barcode is_wt a \\\n", "8 1 A GCA sub CAGTGTCACAGACTGTCTCA 0 0.846289 \n", "9 1 A GCA sub CAGTGTCAGAGACTCACTCT 0 0.511128 \n", "10 1 A GCC sub GTCTCAGAGACACAGTGTGT 0 0.675473 \n", "11 1 A GCC sub TGACTCAGAGTCTCACAGAG 0 0.518410 \n", "12 1 A GCG sub ACTGTCACACACTCTCTGAG 0 1.064481 \n", "... ... .. ... ... ... ... ... \n", "80831 621 W TGG sub GACAGTCACTCAGTGAGACT 0 0.805424 \n", "80832 621 Y TAC sub CTGACTGTGTCTCTCACACT 0 0.690574 \n", "80833 621 Y TAC sub GACAGACTCTCTCTGTGTGA 0 0.950902 \n", "80834 621 Y TAT sub GACTGTCAGTCTCTCTCTGA 0 0.846289 \n", "80835 621 Y TAT sub TGTCTCAGTGAGTCTCTGAC 0 0.610418 \n", "\n", " b \n", "8 1.032180 \n", "9 1.132121 \n", "10 1.059506 \n", "11 0.789974 \n", "12 0.725189 \n", "... ... \n", "80831 0.843358 \n", "80832 0.691127 \n", "80833 0.712097 \n", "80834 1.251792 \n", "80835 1.282817 \n", "\n", "[75796 rows x 8 columns]" ], "text/html": [ "\n", "
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abs_posaacodonlib_typebarcodeis_wtab
81AGCAsubCAGTGTCACAGACTGTCTCA00.8462891.032180
91AGCAsubCAGTGTCAGAGACTCACTCT00.5111281.132121
101AGCCsubGTCTCAGAGACACAGTGTGT00.6754731.059506
111AGCCsubTGACTCAGAGTCTCACAGAG00.5184100.789974
121AGCGsubACTGTCACACACTCTCTGAG01.0644810.725189
...........................
80831621WTGGsubGACAGTCACTCAGTGAGACT00.8054240.843358
80832621YTACsubCTGACTGTGTCTCTCACACT00.6905740.691127
80833621YTACsubGACAGACTCTCTCTGTGTGA00.9509020.712097
80834621YTATsubGACTGTCAGTCTCTCTCTGA00.8462891.251792
80835621YTATsubTGTCTCAGTGAGTCTCTGAC00.6104181.282817
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75796 rows × 8 columns

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\n" ] }, "metadata": {}, "execution_count": 7 } ] }, { "cell_type": "code", "source": [ "# check if any fitness values are inf\n", "fitness_data_sub.loc[(fitness_data_sub['a'] == np.inf)|(fitness_data_sub['b'] == np.inf)].head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 112 }, "id": "30By450K6Wlv", "outputId": "c99ac25a-b194-45ae-badd-2ffa9ed2eea7" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " abs_pos aa codon lib_type barcode is_wt a b\n", "4784 38 G GGG sub AGACACTGAGTGAGAGAGTC 0 inf inf\n", "21505 166 S TCA sub CTGAGAGTGAGACACACAGA 0 inf inf" ], "text/html": [ "\n", "
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abs_posaacodonlib_typebarcodeis_wtab
478438GGGGsubAGACACTGAGTGAGAGAGTC0infinf
21505166STCAsubCTGAGAGTGAGACACACAGA0infinf
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abs_posaacodonlib_typebarcodeis_wtab
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\n" ] }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "# Replacing infinite with nan\n", "fitness_data_sub.replace([np.inf, -np.inf], np.nan, inplace=True)\n", "\n", "# Dropping all the rows with nan values\n", "fitness_data_sub.dropna(inplace=True)" ], "metadata": { "id": "Ms_zll1Y8LKx" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "fitness_data_sub # 40 rows containing infinite fitness values should be dropped" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "tDjx2mFk8Q73", "outputId": "ecfb19f6-8895-44db-a7a8-60246b51fc70" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " abs_pos aa codon lib_type barcode is_wt a \\\n", "8 1 A GCA sub CAGTGTCACAGACTGTCTCA 0 0.846289 \n", "9 1 A GCA sub CAGTGTCAGAGACTCACTCT 0 0.511128 \n", "10 1 A GCC sub GTCTCAGAGACACAGTGTGT 0 0.675473 \n", "11 1 A GCC sub TGACTCAGAGTCTCACAGAG 0 0.518410 \n", "12 1 A GCG sub ACTGTCACACACTCTCTGAG 0 1.064481 \n", "... ... .. ... ... ... ... ... \n", "80831 621 W TGG sub GACAGTCACTCAGTGAGACT 0 0.805424 \n", "80832 621 Y TAC sub CTGACTGTGTCTCTCACACT 0 0.690574 \n", "80833 621 Y TAC sub GACAGACTCTCTCTGTGTGA 0 0.950902 \n", "80834 621 Y TAT sub GACTGTCAGTCTCTCTCTGA 0 0.846289 \n", "80835 621 Y TAT sub TGTCTCAGTGAGTCTCTGAC 0 0.610418 \n", "\n", " b \n", "8 1.032180 \n", "9 1.132121 \n", "10 1.059506 \n", "11 0.789974 \n", "12 0.725189 \n", "... ... \n", "80831 0.843358 \n", "80832 0.691127 \n", "80833 0.712097 \n", "80834 1.251792 \n", "80835 1.282817 \n", "\n", "[75794 rows x 8 columns]" ], "text/html": [ "\n", "
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abs_posaacodonlib_typebarcodeis_wtab
81AGCAsubCAGTGTCACAGACTGTCTCA00.8462891.032180
91AGCAsubCAGTGTCAGAGACTCACTCT00.5111281.132121
101AGCCsubGTCTCAGAGACACAGTGTGT00.6754731.059506
111AGCCsubTGACTCAGAGTCTCACAGAG00.5184100.789974
121AGCGsubACTGTCACACACTCTCTGAG01.0644810.725189
...........................
80831621WTGGsubGACAGTCACTCAGTGAGACT00.8054240.843358
80832621YTACsubCTGACTGTGTCTCTCACACT00.6905740.691127
80833621YTACsubGACAGACTCTCTCTGTGTGA00.9509020.712097
80834621YTATsubGACTGTCAGTCTCTCTCTGA00.8462891.251792
80835621YTATsubTGTCTCAGTGAGTCTCTGAC00.6104181.282817
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75794 rows × 8 columns

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abs_posaaab
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41F[0.7435612728322876, 0.4087579206132755, 0.611...[0.807728060386305, 0.4996692588497194, 0.6489...
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12399 rows × 4 columns

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abs_posaaabfitness_values
01A[0.846288745135642, 0.5111275214408407, 0.6754...[1.0321796826964167, 1.1321210764812213, 1.059...[0.846288745135642, 0.5111275214408407, 0.6754...
11C[0.4204436063464981, 0.5026736592340829, 0.381...[0.8655702836113958, 0.2399670461068991, 1.113...[0.4204436063464981, 0.5026736592340829, 0.381...
21D[0.3147959865123928, 0.5590239058996285, 1.036...[0.2239188297881254, 0.1296074941648796, 0.396...[0.3147959865123928, 0.5590239058996285, 1.036...
31E[0.7982236642760111, 0.5118219881819288, 0.920...[1.9689414343438607, 0.4762955710543403, 0.510...[0.7982236642760111, 0.5118219881819288, 0.920...
41F[0.7435612728322876, 0.4087579206132755, 0.611...[0.807728060386305, 0.4996692588497194, 0.6489...[0.7435612728322876, 0.4087579206132755, 0.611...
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abs_posaaabfitness_valuesis_wt_aaaa_mutations
01A[0.846288745135642, 0.5111275214408407, 0.6754...[1.0321796826964167, 1.1321210764812213, 1.059...[0.846288745135642, 0.5111275214408407, 0.6754...0M1A
11C[0.4204436063464981, 0.5026736592340829, 0.381...[0.8655702836113958, 0.2399670461068991, 1.113...[0.4204436063464981, 0.5026736592340829, 0.381...0M1C
21D[0.3147959865123928, 0.5590239058996285, 1.036...[0.2239188297881254, 0.1296074941648796, 0.396...[0.3147959865123928, 0.5590239058996285, 1.036...0M1D
31E[0.7982236642760111, 0.5118219881819288, 0.920...[1.9689414343438607, 0.4762955710543403, 0.510...[0.7982236642760111, 0.5118219881819288, 0.920...0M1E
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12399 rows × 7 columns

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\n" ] }, "metadata": {}, "execution_count": 18 } ] }, { "cell_type": "markdown", "source": [ "### EDA of fitness values" ], "metadata": { "id": "R4tLSwBu_gfE" } }, { "cell_type": "code", "source": [ "# All the fitness values for wildtype sequences\n", "fitness_values_wt = np.concatenate(fitness_data_grouped.query('is_wt_aa == 1')['fitness_values'].values)\n", "# Fitness values for all the mutant sequences\n", "fitness_values_mutant = np.concatenate(fitness_data_grouped.query('is_wt_aa == 0')['fitness_values'].values)" ], "metadata": { "id": "nRWuI7lN99-6" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# Density plot of fitness values for wildtype vs mutants\n", "sns.histplot(np.log10(fitness_values_wt), label = \"wildtype sequences\", kde=True)\n", "sns.histplot(np.log10(fitness_values_mutant), label = \"mutant sequences\", kde=True)\n", "plt.xlabel('log10(fitness)')\n", "plt.ylabel('Density')\n", "plt.title(\"Distribution of fitness values: wild-type vs mutant sequences\")\n", "plt.legend();" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 553 }, "id": "oQmLv2-N_uZ5", "outputId": "9dc9ff37-1d46-48c1-d4ad-ae270a8e3582" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":2: RuntimeWarning: divide by zero encountered in log10\n", " sns.histplot(np.log10(fitness_values_wt), label = \"wildtype sequences\", kde=True)\n", ":3: RuntimeWarning: divide by zero encountered in log10\n", " sns.histplot(np.log10(fitness_values_mutant), label = \"mutant sequences\", kde=True)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# Density plot of fitness values for wildtype vs mutants\n", "sns.histplot(fitness_values_wt, label = \"wild-type sequences\", kde=True)\n", "sns.histplot(fitness_values_mutant, label = \"mutant sequences\", kde=True)\n", "plt.xlabel('fitness')\n", "plt.ylabel('Density')\n", "plt.legend()\n", "plt.title(\"Distribution of fitness values: wild-type vs mutant sequences\");" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 484 }, "id": "gTyTsg3O_u58", "outputId": "b0b86f74-a519-4502-b89d-64b6be51ae38" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# Find the row with max wildtype fitness value\n", "fitness_data_sub.loc[(fitness_data_sub['a'] == max(fitness_values_wt))|\n", " (fitness_data_sub['b'] == max(fitness_values_wt))]" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 81 }, "id": "AcDOasXkAKvk", "outputId": "8bab519f-7160-41ae-86e3-a21c4b3e6a72" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " abs_pos aa codon lib_type barcode is_wt a \\\n", "16466 128 L CTA sub CACACAGTCTGACTGACTGT 0 1.493451 \n", "\n", " b \n", "16466 20.204368 " ], "text/html": [ "\n", "
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abs_posaacodonlib_typebarcodeis_wtab
16466128LCTAsubCACACAGTCTGACTGACTGT01.49345120.204368
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abs_posaacodonlib_typebarcodeis_wtab
789362PCCTsubAGACAGAGAGAGAGTCTGTC02.613539105.853321
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aa_mutationsfitness_values
-1[0.4618301444042837, 1.8924231230151436, 0.952...
0M1A[0.846288745135642, 0.5111275214408407, 0.6754...
1M1C[0.4204436063464981, 0.5026736592340829, 0.381...
2M1D[0.3147959865123928, 0.5590239058996285, 1.036...
3M1E[0.7982236642760111, 0.5118219881819288, 0.920...
.........
12394Q621S[0.932344002063711, 2.0666804201404507, 1.4131...
12395Q621T[0.6892849507892106, 1.4358430931469086, 1.540...
12396Q621V[0.7822837139909294, 1.1420505557193783, 1.292...
12397Q621W[0.7368458657141186, 0.8054241354743774, 0.643...
12398Q621Y[0.6905742978726174, 0.9509024433834408, 0.846...
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11800 rows × 2 columns

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\n" ] }, "metadata": {}, "execution_count": 24 } ] }, { "cell_type": "code", "source": [ "# Inspired from https://github.com/ElArkk/jax-unirep/blob/master/paper/gfp_prediction.ipynb\n", "def mut2seq(mutation_string, wt_sequence, delimiter=\":\"):\n", " \"\"\"\n", " Reconstruct full mutant sequence given mutation string.\n", "\n", " Example mutation_string:\n", " - P2C\n", " - P2T; G3A\n", " - A111T; Q194R; N249I; N251Y; H255Y\n", "\n", " Example wt_seq:\n", " 'MPGFYEIVIKVP'\n", "\n", " Example Output for mutation_string P2C:\n", " 'MPGFYEIVIKVP' -> 'MCGFYEIVIKVP'\n", " \"\"\"\n", " pattern = '([A-Z])(\\d+)([A-Z])'\n", " if mutation_string is None or mutation_string == \"\":\n", " return wt_sequence\n", "\n", " mutations = mutation_string.split(delimiter)\n", " mutant_sequence = wt_sequence # mutant_sequence is a list\n", " for mut in mutations:\n", " match = re.search(pattern, mut) # Ensure mutation string matches regex pattern\n", " if not match: raise ValueError(f\"\"\" The mutation string {mut} is invalid.\"\"\")\n", " else:\n", " position = int(match.group(2)) # Return mutation position from mutation string.\n", " letter = match.group(3) # Return mutation letter from mutation string.\n", " if position == 0:\n", " raise ValueError(\n", " f\"\"\"\n", " The mutation string {mut} is invalid.\n", " It has \"0\" as its position.\n", " \"\"\"\n", " )\n", " if position > len(wt_sequence):\n", " raise ValueError(\n", " f\"\"\"\n", " The mutation string {mut} is invalid.\n", " Its position is greater than the length of the WT sequence.\n", " \"\"\"\n", " )\n", " mutant_sequence = mutant_sequence[:position-1] + letter + mutant_sequence[position:]\n", " # -1 is necessary because the python string is zero-indexed\n", " return mutant_sequence" ], "metadata": { "id": "NSsu5KsnBceE" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "mut2rep78 = partial(mut2seq, wt_sequence=wt_seq)" ], "metadata": { "id": "fq8XXTOVBJF6" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "fitness_by_mutation['sequence'] = fitness_by_mutation['aa_mutations'].apply(mut2rep78)\n", "fitness_by_mutation['num_fitval'] = fitness_by_mutation['fitness_values'].apply(len)\n", "fitness_by_mutation['median_fitness'] = fitness_by_mutation['fitness_values'].apply(np.median)\n", "fitness_by_mutation['std'] = fitness_by_mutation['fitness_values'].apply(np.std)\n", "fitness_by_mutation[\"log10_fitness\"] = fitness_by_mutation['median_fitness'].apply(np.log10)\n", "fitness_by_mutation" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 791 }, "id": "GZXPXDQYBeZJ", "outputId": "19f3b6bf-9fc1-4afd-b20e-1018552f15b9" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " aa_mutations fitness_values \\\n", "-1 [0.4618301444042837, 1.8924231230151436, 0.952... \n", " 0 M1A [0.846288745135642, 0.5111275214408407, 0.6754... \n", " 1 M1C [0.4204436063464981, 0.5026736592340829, 0.381... \n", " 2 M1D [0.3147959865123928, 0.5590239058996285, 1.036... \n", " 3 M1E [0.7982236642760111, 0.5118219881819288, 0.920... \n", "... ... ... \n", " 12394 Q621S [0.932344002063711, 2.0666804201404507, 1.4131... \n", " 12395 Q621T [0.6892849507892106, 1.4358430931469086, 1.540... \n", " 12396 Q621V [0.7822837139909294, 1.1420505557193783, 1.292... \n", " 12397 Q621W [0.7368458657141186, 0.8054241354743774, 0.643... \n", " 12398 Q621Y [0.6905742978726174, 0.9509024433834408, 0.846... \n", "\n", " sequence num_fitval \\\n", "-1 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... 6270 \n", " 0 APGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... 16 \n", " 1 CPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... 8 \n", " 2 DPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... 8 \n", " 3 EPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... 8 \n", "... ... ... \n", " 12394 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... 24 \n", " 12395 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... 16 \n", " 12396 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... 16 \n", " 12397 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... 4 \n", " 12398 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... 8 \n", "\n", " median_fitness std log10_fitness \n", "-1 0.933207 0.670053 -0.030022 \n", " 0 0.838670 0.248496 -0.076409 \n", " 1 0.635236 0.347019 -0.197065 \n", " 2 0.477791 0.284308 -0.320762 \n", " 3 0.735683 0.461709 -0.133309 \n", "... ... ... ... \n", " 12394 1.077322 0.340245 0.032346 \n", " 12395 0.928285 0.463962 -0.032319 \n", " 12396 0.993901 0.308860 -0.002657 \n", " 12397 0.771135 0.076128 -0.112870 \n", " 12398 0.779193 0.244713 -0.108355 \n", "\n", "[11800 rows x 7 columns]" ], "text/html": [ "\n", "
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aa_mutationsfitness_valuessequencenum_fitvalmedian_fitnessstdlog10_fitness
-1[0.4618301444042837, 1.8924231230151436, 0.952...MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...62700.9332070.670053-0.030022
0M1A[0.846288745135642, 0.5111275214408407, 0.6754...APGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...160.8386700.248496-0.076409
1M1C[0.4204436063464981, 0.5026736592340829, 0.381...CPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...80.6352360.347019-0.197065
2M1D[0.3147959865123928, 0.5590239058996285, 1.036...DPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...80.4777910.284308-0.320762
3M1E[0.7982236642760111, 0.5118219881819288, 0.920...EPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...80.7356830.461709-0.133309
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12394Q621S[0.932344002063711, 2.0666804201404507, 1.4131...MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...241.0773220.3402450.032346
12395Q621T[0.6892849507892106, 1.4358430931469086, 1.540...MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...160.9282850.463962-0.032319
12396Q621V[0.7822837139909294, 1.1420505557193783, 1.292...MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...160.9939010.308860-0.002657
12397Q621W[0.7368458657141186, 0.8054241354743774, 0.643...MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...40.7711350.076128-0.112870
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11800 rows × 7 columns

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aa_mutationsfitness_valuessequencenum_fitvalmedian_fitnessstdlog10_fitness
20P2A[0.7253903529734075, 0.6094350499130314, 0.886...MAGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...160.9402080.457751-0.026776
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aa_mutationsfitness_valuessequencenum_fitvalmedian_fitnessstdlog10_fitness
-1[0.4618301444042837, 1.8924231230151436, 0.952...MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...62700.9332070.670053-0.030022
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1M1C[0.4204436063464981, 0.5026736592340829, 0.381...CPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...80.6352360.347019-0.197065
2M1D[0.3147959865123928, 0.5590239058996285, 1.036...DPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...80.4777910.284308-0.320762
3M1E[0.7982236642760111, 0.5118219881819288, 0.920...EPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...80.7356830.461709-0.133309
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\n" ] }, "metadata": {}, "execution_count": 32 } ] }, { "cell_type": "markdown", "source": [ "To recapitulate here is a brief description of the columns in the dataset:\n", "\n", "**`aa_mutations`**: Indicates amino acid mutations, where, for example, \"M1A\" signifies a change from amino acid \"M\" at position 1 to amino acid \"A\". A blank line at row index -1 denotes no amino acid mutation, representing the wild-type sequence.\n", "\n", "**`fitness_values`**: Represents the fitness values corresponding to each protein variant.\n", "\n", "**`sequence`**: Displays the wild-type protein sequence at index -1 and mutated protein sequences for all other indices.\n", "\n", "**`num_fitval`**: Indicates the number of fitness values associated with each protein variant.\n", "\n", "**`median_fitness`**: Represents the median of the num_fitval column.\n", "\n", "**`std`**: Signifies the standard deviation of the num_fitval column.\n", "\n", "**`log10_fitness`**: Reflects the log10 transformation of the median_fitness column." ], "metadata": { "id": "ODtTPeB2Et-z" } }, { "cell_type": "code", "source": [ "sns.histplot(np.log10(fitness_by_mutation['median_fitness'].values), kde=True)\n", "plt.xlabel('log10(fitness)')\n", "plt.ylabel('Density')\n", "plt.title(\"Distribution of median fitness values\");" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 519 }, "id": "84JXfHPZBvi2", "outputId": "445f59d0-2d40-4e3b-8fa0-15e59077cdbe" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ ":1: RuntimeWarning: divide by zero encountered in log10\n", " sns.histplot(np.log10(fitness_by_mutation['median_fitness'].values), kde=True)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "sns.histplot(fitness_by_mutation['median_fitness'].values, kde=True)\n", "plt.xlabel('fitness')\n", "plt.ylabel('Density')\n", "plt.title(\"Distribution of median fitness values\");" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 484 }, "id": "D-Y_VKuXBwTC", "outputId": "c3717e97-2ffb-41a8-e627-5109a3c7daed" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "# median_fitness_wt value is used to normalize the entire dataset\n", "# such that wt fitness corresponds to a value of 1\n", "median_fitness_wt = fitness_by_mutation.loc[-1, 'median_fitness']\n", "\n", "sequences = fitness_by_mutation[\"sequence\"].tolist()\n", "fitness = fitness_by_mutation.loc[:, 'median_fitness'].values # obtain target\n", "\n", "# transformation to normalize WT fitness value to 1\n", "fitness_norm = (fitness - np.min(fitness))/(median_fitness_wt -np.min(fitness))" ], "metadata": { "id": "jV2C5p6SB3FY" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "sns.histplot(fitness_norm, kde=True)\n", "plt.xlabel('fitness')\n", "plt.ylabel('Density')\n", "plt.title(\"Distribution of median fitness values: normalized\");" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 484 }, "id": "dlLq8Z6DB3vk", "outputId": "c85e26d5-b07c-4561-a9d9-4c3fd54cdf9b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## Tokenizing the data" ], "metadata": { "id": "K6uQ3AT9m5qq" } }, { "cell_type": "markdown", "source": [ "All inputs to neural networks must be numerical. The procedure of converting strings into numerical indices, rendering them suitable for neural networks, is termed **tokenization**. In protein language models, each amino acid is converted to a single token. Every model on `transformers` comes with an associated `tokenizer` that handles tokenization for it. This holds true for protein language models as well." ], "metadata": { "id": "0TFH2e53nA91" } }, { "cell_type": "code", "source": [ "esm1v_checkpoint = \"facebook/esm1v_t33_650M_UR90S_1\"" ], "metadata": { "id": "c-vVXS6qD4eM" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# The AutoTokenizer class automatically retrieve the model's configuration, pretrained weights,\n", "# or vocabulary from the name of the checkpoint.\n", "from transformers import AutoTokenizer\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(esm1v_checkpoint)" ], "metadata": { "id": "TU-lrYRUn0Xo", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "c18fc93a-4cf4-47f1-8e59-a751ef7831d4" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] } ] }, { "cell_type": "code", "source": [ "# tokenize a single sequence\n", "tokenizer(sequences[0])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "QMooDFCmoE15", "outputId": "f20b8ba6-e41f-477c-f8ec-35b7287784a1" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "{'input_ids': [0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14, 8, 13, 4, 13, 6, 21, 4, 14, 6, 12, 8, 13, 8, 18, 7, 17, 22, 7, 5, 9, 15, 9, 22, 9, 4, 14, 14, 13, 8, 13, 20, 13, 4, 17, 4, 12, 9, 16, 5, 14, 4, 11, 7, 5, 9, 15, 4, 16, 10, 13, 18, 4, 11, 9, 22, 10, 10, 7, 8, 15, 5, 14, 9, 5, 4, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]}" ] }, "metadata": {}, "execution_count": 39 } ] }, { "cell_type": "markdown", "source": [ "Our sequence has been converted into input_ids, which is the tokenized sequence, and an attention_mask. The purpose of the attention mask is to manage variable-length sequences. In instances of shorter sequences, padding is applied using blank \"padding\" tokens, and the attention mask is padded with 0s, signaling the model to disregard these tokens during processing." ], "metadata": { "id": "KfoOP5-0CMXQ" } }, { "cell_type": "code", "source": [ "tokz = tokenizer(sequences)" ], "metadata": { "id": "PjFq0UFBCjPH" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## Pytorch compatible `Dataset` creation" ], "metadata": { "id": "fLup4mwRCpTY" } }, { "cell_type": "markdown", "source": [ "Our next step involves shaping this data into a dataset compatible with PyTorch. To achieve this, we leverage the HuggingFace `Dataset` class." ], "metadata": { "id": "rBC2zHfuDbL-" } }, { "cell_type": "code", "source": [ "from datasets import Dataset\n", "ds = Dataset.from_dict(tokz)\n", "\n", "ds" ], "metadata": { "id": "OsEkvRSkCo1J", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "b4216edb-6a2c-496b-c659-7876ef90a168" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['input_ids', 'attention_mask'],\n", " num_rows: 11800\n", "})" ] }, "metadata": {}, "execution_count": 41 } ] }, { "cell_type": "code", "source": [ "ds = ds.add_column(\"labels\", fitness_norm)\n", "ds" ], "metadata": { "id": "J5UuVDG2ClG7", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "37f7a4b2-f323-4f3e-8567-e77898704bc7" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['input_ids', 'attention_mask', 'labels'],\n", " num_rows: 11800\n", "})" ] }, "metadata": {}, "execution_count": 42 } ] }, { "cell_type": "code", "source": [ "print(ds[0]);" ], "metadata": { "id": "Viv7iWjwdsdf", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "53dde6d3-219c-4645-f1cd-c0349454024e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'input_ids': [0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14, 8, 13, 4, 13, 6, 21, 4, 14, 6, 12, 8, 13, 8, 18, 7, 17, 22, 7, 5, 9, 15, 9, 22, 9, 4, 14, 14, 13, 8, 13, 20, 13, 4, 17, 4, 12, 9, 16, 5, 14, 4, 11, 7, 5, 9, 15, 4, 16, 10, 13, 18, 4, 11, 9, 22, 10, 10, 7, 8, 15, 5, 14, 9, 5, 4, 18, 18, 7, 16, 18, 9, 15, 6, 9, 8, 19, 18, 21, 20, 21, 7, 4, 7, 9, 11, 11, 6, 7, 15, 8, 20, 7, 4, 6, 10, 18, 4, 8, 16, 12, 10, 9, 15, 4, 12, 16, 10, 12, 19, 10, 6, 12, 9, 14, 11, 4, 14, 17, 22, 18, 5, 7, 11, 15, 11, 10, 17, 6, 5, 6, 6, 6, 17, 15, 7, 7, 13, 9, 23, 19, 12, 14, 17, 19, 4, 4, 14, 15, 11, 16, 14, 9, 4, 16, 22, 5, 22, 11, 17, 20, 9, 16, 19, 4, 8, 5, 23, 4, 17, 4, 11, 9, 10, 15, 10, 4, 7, 5, 16, 21, 4, 11, 21, 7, 8, 16, 11, 16, 9, 16, 17, 15, 9, 17, 16, 17, 14, 17, 8, 13, 5, 14, 7, 12, 10, 8, 15, 11, 8, 5, 10, 19, 6, 9, 4, 7, 6, 22, 4, 7, 13, 15, 6, 12, 11, 8, 9, 15, 16, 22, 12, 16, 9, 13, 16, 5, 8, 19, 12, 8, 18, 17, 5, 5, 8, 17, 8, 10, 8, 16, 12, 15, 5, 5, 4, 13, 17, 5, 6, 15, 12, 20, 8, 4, 11, 15, 11, 5, 14, 13, 19, 4, 7, 6, 16, 16, 14, 7, 9, 13, 12, 8, 8, 17, 10, 12, 19, 15, 12, 4, 9, 4, 17, 6, 19, 13, 14, 16, 19, 5, 5, 8, 7, 18, 4, 6, 22, 5, 11, 15, 15, 18, 6, 15, 10, 17, 11, 12, 22, 4, 18, 6, 14, 5, 11, 11, 6, 15, 11, 17, 12, 5, 9, 5, 12, 5, 21, 11, 7, 14, 18, 19, 6, 23, 7, 17, 22, 11, 17, 9, 17, 18, 14, 18, 17, 13, 23, 7, 13, 15, 20, 7, 12, 22, 22, 9, 9, 6, 15, 20, 11, 5, 15, 7, 7, 9, 8, 5, 15, 5, 12, 4, 6, 6, 8, 15, 7, 10, 7, 13, 16, 15, 23, 15, 8, 8, 5, 16, 12, 13, 14, 11, 14, 7, 12, 7, 11, 8, 17, 11, 17, 20, 23, 5, 7, 12, 13, 6, 17, 8, 11, 11, 18, 9, 21, 16, 16, 14, 4, 16, 13, 10, 20, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 'labels': 1.0}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Transformers as feature extractors\n", "\n", "(This section closely follows [Chapter 02 of Natural Language Processing with Transformers book](https://github.com/nlp-with-transformers/notebooks/blob/main/02_classification.ipynb).)" ], "metadata": { "id": "N6Vx5nEoF1wd" } }, { "cell_type": "markdown", "source": [ "To use transformers as feature extractors, we use the transformers' hidden states as input features and train a classifier on them. We do not modify the pretrained model (the body's weights are frozen during training)." ], "metadata": { "id": "TWDt_WlIHIgA" } }, { "cell_type": "markdown", "source": [ "### Using pretrained models" ], "metadata": { "id": "H4jBvKksGRgj" } }, { "cell_type": "code", "source": [ "# Similar to the AutoTokenizer class, AutoModel has a from_pretrained() method\n", "# to load the weights of a pretrained model\n", "from transformers import AutoModel\n", "import torch\n", "\n", "esm1v_checkpoint = \"facebook/esm1v_t33_650M_UR90S_1\"\n", "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", "model = AutoModel.from_pretrained(esm1v_checkpoint).to(device)" ], "metadata": { "id": "PlenQaJHGX_q", "colab": { "base_uri": "https://localhost:8080/", "height": 104, "referenced_widgets": [ "11b9a515da7a4ba79a298c3a1843d909", "4eae7c15d046420bbe418f201059d173", "3875367e6a464301bdfb1cdb91b22954", "84264ce6dbb04b86aab56731f545d74e", "6be62a25508d41f992d7dd48a493ee9a", "882dfaa57ee74e6eb949ee3ae6a54b14", "7c017d48def6470caecf1458f5ae21c1", "c5de84b684944fa6ad5c52b14ddc183e", "169da0f51bb24c6db514a91d7ebd41b9", "0c6cf5ae4c0a437b88b19cd9ae47963c", "4d011a8825cc46aa9b051f3dd1015085" ] }, "outputId": "f74fca1c-eee5-482d-df56-110a690ca577" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "pytorch_model.bin: 0%| | 0.00/2.61G [00:00" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "sns.histplot(y_test, kde=True)\n", "plt.xlabel('fitness')\n", "plt.ylabel('Density')\n", "plt.title(\"Distribution of test fitness values\");" ], "metadata": { "id": "xOzCg_lNhZ6g", "colab": { "base_uri": "https://localhost:8080/", "height": 484 }, "outputId": "717fb701-3e92-4f29-8eb5-22a2e0ea0333" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## Visualizing the training set" ], "metadata": { "id": "-8G1VQvPI0MA" } }, { "cell_type": "markdown", "source": [ "Prior to training a model on the hidden states, it is advisable to conduct a swift visualization. This step ensures that the the hidden states indeed encapsulate meaningful representation for the sequences we want to classify." ], "metadata": { "id": "a1BLZblLh-tq" } }, { "cell_type": "markdown", "source": [ "### PCA" ], "metadata": { "id": "4tEhnAS9I6DA" } }, { "cell_type": "markdown", "source": [ "PCA, or Principal Component Analysis, is a technique for reducing the dimensionality of data. By transforming original features into uncorrelated variables called principal components, PCA simplifies data representation while retaining key information." ], "metadata": { "id": "KiMMXJcfrBi_" } }, { "cell_type": "markdown", "source": [ "Here, we plot the first two principal components on the x- and y- axes. Each point is then colored by its scaled effect (what we want to predict).\n", "\n", "Visually, we can see a separation based on color/effect, suggesting that our representations are useful for this task, without any task-specific training!" ], "metadata": { "id": "kWfhqg-_i7-v" } }, { "cell_type": "code", "source": [ "from sklearn.decomposition import PCA\n", "num_pca_components = 60\n", "pca = PCA(num_pca_components)\n", "X_train_pca = pca.fit_transform(X_train)" ], "metadata": { "id": "VgPb6CXBi7NS" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "fig_dims = (7, 6)\n", "fig, ax = plt.subplots(figsize=fig_dims)\n", "sc = ax.scatter(X_train_pca[:,0], X_train_pca[:,1], c=y_train, marker='.')\n", "ax.set_xlabel('PCA first principal component')\n", "ax.set_ylabel('PCA second principal component')\n", "plt.colorbar(sc, label='Variant Effect');" ], "metadata": { "id": "rXhFmkl7jBUE", "colab": { "base_uri": "https://localhost:8080/", "height": 553 }, "outputId": "da023f6e-2e0a-4f2d-e10a-dcd201c94576" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "### UMAP" ], "metadata": { "id": "988-gjIhI7eZ" } }, { "cell_type": "markdown", "source": [ "Uniform Manifold Approximation and Projection (UMAP) is a powerful dimensionality reduction technique and manifold learning algorithm. Similar to t-SNE but computationally more efficient, UMAP maps high-dimensional data to a lower-dimensional space while preserving the inherent structure and relationships within the data.\n", "\n", "PCA focuses on capturing the maximum variance in the data by finding the principal components, which are linear combinations of the original features. On the other hand, UMAP specializes in preserving local and global structures within the data, making it adept at capturing non-linear relationships." ], "metadata": { "id": "44ulEKNArfuc" } }, { "cell_type": "markdown", "source": [ "We use UMAP algorithm to project the vectors down to 2D." ], "metadata": { "id": "E9uXnpdijFGx" } }, { "cell_type": "code", "source": [ "from umap import UMAP\n", "from sklearn.preprocessing import MinMaxScaler\n", "\n", "def get_umap_embedding(features, umap_params):\n", "\n", " # Initialize UMAP\n", " reducer = UMAP(random_state=7, **umap_params)\n", " # Fit UMAP\n", " mapper = reducer.fit(features)\n", " embedding = mapper.transform(features)\n", " return embedding\n", "\n", "umap_params = {\"n_neighbors\": 15, \"min_dist\": 0.1, \"metric\": \"euclidean\"}" ], "metadata": { "id": "OpJpZwedjF0E" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "import matplotlib.colors as colors\n", "\n", "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(6, 6))\n", "\n", "# Scale features to [0,1] range\n", "X_scaled = MinMaxScaler().fit_transform(X_train)\n", "\n", "embedding = get_umap_embedding(X_scaled, umap_params=umap_params)\n", "divnorm = colors.TwoSlopeNorm(vmin=y_train.min(), vcenter=1, vmax=y_train.max())\n", "\n", "ax.scatter(embedding[:, 0], embedding[:, 1], c=y_train, cmap=\"coolwarm\", norm=divnorm,\n", " s=10.0, alpha=1.0, marker=\"o\", linewidth=0)\n", "ax.set(xlabel=\"UMAP Dim 0\", ylabel=\"UMAP Dim 1\")\n", "\n", "plt.tight_layout()\n", "plt.show();" ], "metadata": { "id": "totJHnKgjQgP", "colab": { "base_uri": "https://localhost:8080/", "height": 648 }, "outputId": "19703ea1-6254-465d-c1da-a7e140ed7a80" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1943: UserWarning: n_jobs value -1 overridden to 1 by setting random_state. Use no seed for parallelism.\n", " warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "## Training a regressor" ], "metadata": { "id": "jpWuzuIxJTWs" } }, { "cell_type": "markdown", "source": [ "### RidgeCV" ], "metadata": { "id": "-q2zQPE_KrgP" } }, { "cell_type": "markdown", "source": [ "Ridge. This is L2-penalized linear regression. We used the Python ‘sklearn. linear_model.RidgeCV’ implementation to perform tenfold cross-validation (on the input training data) to select a level of regularization (the parameter α) that minimizes held-out mean squared error. The schedule of regularization strengths was set to be logarithmically spaced from $1 × 10^{−6}$ to $1 × 10^{6}.$" ], "metadata": { "id": "K1y4Vtb9exGO" } }, { "cell_type": "code", "source": [ "from sklearn.linear_model import RidgeCV\n", "from sklearn.model_selection import KFold\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.metrics import mean_squared_error, PredictionErrorDisplay, ndcg_score\n", "from sklearn.pipeline import make_pipeline" ], "metadata": { "id": "njbhS4MwjgsW" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "%%capture\n", "# different alphas (regularization parameter) to try\n", "alphas = np.logspace(-6, 6, 100)\n", "kfold = KFold(n_splits=5, random_state=42, shuffle=True) # K-Folds cross-validator\n", "\n", "# define model\n", "ridgecv = make_pipeline(StandardScaler(),\n", " RidgeCV(alphas=alphas, scoring='neg_mean_squared_error', cv=kfold))\n", "\n", "# fit model\n", "ridgecv.fit(X_train, y_train)\n", "\n", "ridge_best_alpha = ridgecv[1].alpha_\n", "\n", "# make predictions\n", "preds_train = ridgecv.predict(X_train)\n", "preds_test = ridgecv.predict(X_test);" ], "metadata": { "id": "Y0W_KmOLjlra", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "42cdfced-ae0d-4005-959d-b0ce3402d2d0", "collapsed": true }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.24409e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.23889e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17692e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.6995e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.28037e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.82848e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.55469e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.69686e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.6995e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.28037e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.82848e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.55469e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.69686e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.6995e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.28037e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.82848e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.55469e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.69686e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.6995e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.28037e-12): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.84029e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.55469e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.69686e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.78737e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.89147e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.63482e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.11724e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.20573e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.78737e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.89147e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.63482e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.11724e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.20573e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.87224e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.26931e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.80272e-11): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.81222e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.37595e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.6015e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.88225e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.44967e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.05204e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.77092e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.252e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.59976e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.10242e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.82486e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.77182e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.63001e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.25373e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.82221e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=6.19317e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.84407e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.89402e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.68043e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.43253e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=7.74541e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.86742e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=7.99673e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.07499e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=9.61754e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.98098e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=8.58426e-10): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.19893e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.08181e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.12131e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.12079e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.20505e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.66454e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.34032e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.42253e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.61084e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.20275e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.9917e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.76876e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.83732e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.84286e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.85638e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.6652e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.67328e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.17911e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.38018e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.54891e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.25908e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.38026e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.24782e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.06508e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.263e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.36467e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.28911e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.40405e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.17258e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.24048e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.75119e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.76522e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.91658e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.6269e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.35484e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=7.36402e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=7.29529e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=7.83306e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=7.43244e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=7.28861e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.00807e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=9.75159e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=9.96636e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=9.8188e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=9.64404e-09): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.35762e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.31318e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.33349e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.30966e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.29098e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.76609e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.74778e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.78894e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.69844e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=1.71294e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.35527e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.29767e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.31275e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.24445e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.25838e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.1033e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.02827e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.0784e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.97236e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=2.96664e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.09957e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.0081e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=4.052e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.93615e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=3.9544e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.44857e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.30764e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.36875e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.24531e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n", "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=5.22932e-08): result may not be accurate.\n", " return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n" ] } ] }, { "cell_type": "code", "source": [ "# Regression\n", "from scipy import stats\n", "spearmanr = stats.spearmanr(a=preds_test, b=y_test, axis=0)\n", "print(spearmanr)" ], "metadata": { "id": "nTdAbDCHjsrU", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "3161ffca-e792-4d24-a585-791d59ffb369" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "SignificanceResult(statistic=0.7254197401504189, pvalue=0.0)\n" ] } ] }, { "cell_type": "code", "source": [ "print(ridge_best_alpha)" ], "metadata": { "id": "uG9gLYOqjvZP", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "8cc086d2-b827-49a3-e73d-c5584c1131ab" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "100.0\n" ] } ] }, { "cell_type": "markdown", "source": [ "### RidgeSR" ], "metadata": { "id": "-XrDYgabKx0a" } }, { "cell_type": "markdown", "source": [ "The RidgeSR procedure implemented below has been described in [Low-N protein engineering with data-efficient deep learning](https://www.nature.com/articles/s41592-021-01100-y) paper from Biswas et al, 2021 published in Nature methods.\n", ">Ridge SR: This is the same as the ‘Ridge’ procedure above, except that we additionally perform a post hoc SR procedure. The ‘Ridge’ top model above chooses a level of regularization that optimizes for model generalizability if\n", "the ultimate test distribution (that is, distant regions of the fitness landscape) resembles the training distribution. However, this is not likely the case. Therefore, we performed a post hoc procedure to choose the strongest regularization such that the cross-validation performance was still statistically equal (by t-test) to the level of regularization we would select through normal cross-validation. This procedure selects a stronger regularization than what would be obtained using the ‘Ridge’ procedure as defined above." ], "metadata": { "id": "r1LZn-aCU8mT" } }, { "cell_type": "code", "source": [ "from sklearn.linear_model import Ridge\n", "from sklearn.model_selection import cross_val_score" ], "metadata": { "id": "v85q7VCrkNXJ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "kfold = KFold(n_splits=5, random_state=42, shuffle=True) # K-Folds cross-validator\n", "def RidgeSR(best_alpha, X_train, y_train, cv=kfold):\n", " '''\n", " Perform a post hoc procedure to choose the strongest regularization such that the\n", " cross-validation performance was still statistically equal (by t-test) to the level of\n", " regularization we would select through normal cross-validation. This procedure selects a\n", " stronger regularization than what would be obtained using the ‘RidgeCV’ procedure implemented before.\n", "\n", " Input:\n", " `best_alpha`: best regularization parameter alpha from RidgeCV procedure\n", " '''\n", " alphas = np.linspace(best_alpha, best_alpha*20, 20)\n", " ridge = make_pipeline(StandardScaler(),\n", " Ridge())\n", "\n", " p_values = []\n", " mse = []\n", "\n", " for a in alphas:\n", " ridge[1].set_params(alpha = a)\n", " scores_posthoc = cross_val_score(ridge, X_train, y_train, scoring='neg_mean_squared_error', cv=kfold, n_jobs=-1)\n", " scores_posthoc = np.absolute(scores_posthoc)\n", "\n", " if a == best_alpha: scores = scores_posthoc\n", "\n", " mse.append(np.mean(scores_posthoc))\n", " p_values.append(stats.ttest_ind(scores, scores_posthoc).pvalue)\n", "\n", " # choose alpha higher than best alpha\n", " # choose a stronger alpha than would be selected by RidgeCV such that cross validation performance\n", " # is still equal to best alpha from RidgeCV\n", " # When p value < 0.10 MSE distribution from stronger alpha is considered the same as from best alpha\n", " alpha_ridgsesr = alphas[np.where(1-np.asarray(p_values) <= 0.90)[0][-1]]\n", " mse_ridgesr = mse[np.where(1-np.asarray(p_values) <= 0.90)[0][-1]]\n", "\n", " # Plot alpha vs MSE and alpha vs p-value\n", " fig, (ax0, ax1) = plt.subplots(figsize=(12, 6), nrows=1, ncols=2)\n", "\n", " ax0.plot(alphas, mse, 'ro-')\n", " ax0.set(xlabel='alphas', ylabel='mean squared error')\n", "\n", " ax1.plot(alphas, p_values, 'bo-')\n", " ax1.set(xlabel='alphas', ylabel='p value')\n", "\n", " fig.suptitle(f'RidgeSR alpha: {alpha_ridgsesr}')\n", "\n", " plt.tight_layout()\n", " plt.show();\n", "\n", " return alpha_ridgsesr" ], "metadata": { "id": "Gd0OrylpkOdQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# best alpha from RidgeCV procedure\n", "best_alpha = ridge_best_alpha\n", "\n", "# selects a stronger regularization than what would be obtained using the ‘RidgeCV’ procedure previously defined\n", "ridgsesr_alpha = RidgeSR(best_alpha, X_train, y_train, cv=kfold)\n", "\n", "# define model\n", "ridge = make_pipeline(StandardScaler(),\n", " Ridge(alpha=ridgsesr_alpha))\n", "\n", "# fit model\n", "ridge.fit(X_train, y_train)\n", "\n", "# make predictions\n", "preds_train = ridge.predict(X_train)\n", "preds_test = ridge.predict(X_test)" ], "metadata": { "id": "wklo6WXxkbcd", "colab": { "base_uri": "https://localhost:8080/", "height": 557 }, "outputId": "c98960ab-61b9-4f6b-bd07-81c4beedcf97" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
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}, "metadata": {} } ] }, { "cell_type": "code", "source": [ "print(stats.spearmanr(a=preds_test, b=y_test, axis=0))" ], "metadata": { "id": "GF8eqBXJkhjV", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "7e5cec1e-0974-4fb6-c741-bd2b111aed04" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "SignificanceResult(statistic=0.7156659724956432, pvalue=0.0)\n" ] } ] }, { "cell_type": "code", "source": [ "print(ridgsesr_alpha)" ], "metadata": { "id": "Wjw1kNkHkiCY", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "6c7e66a1-e4a7-4eb8-a883-ea97091c597c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "400.0\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Bagging Regressor" ], "metadata": { "id": "sEzbHeH7K02s" } }, { "cell_type": "markdown", "source": [ "The Bagging Regressor or Ensembled Ridge SR procedure implemented below has been described in [Low-N protein engineering with data-efficient deep learning](https://www.nature.com/articles/s41592-021-01100-y) paper from Biswas et al, 2021 published in Nature methods.\n", "\n", ">Ensembled Ridge SR: This is the same as the ‘Ridge SR’ procedure above, except that the final top model is an ensemble of Ridge SR top models. The ensemble is composed of 100 members. Each member (a Ridge SR top model) is fit to a bootstrap of the training data (N training points are resampled N times with replacement) and a random subset of the features. The final prediction is an average of all members in the ensemble. The rationale for this approach is\n", "that it is based on consensus of many different Ridge SR models that have different ‘hypotheses’ for how sequence might influence function. Differences in these ‘hypotheses’ are driven by the fact that every bootstrap represents a different plausible instantiation of the training data and that every random subsample of features represents different variables that could influence function." ], "metadata": { "id": "xS73oPq9Vsxy" } }, { "cell_type": "code", "source": [ "from sklearn.ensemble import BaggingRegressor\n", "\n", "# define ridge model with RidgeSR alpha\n", "ridge = make_pipeline(StandardScaler(),\n", " Ridge(alpha=ridgsesr_alpha))\n", "\n", "# define bag model\n", "bag = BaggingRegressor(estimator=ridge,\n", " n_estimators=100,\n", " max_samples=1.0,\n", " max_features=1.0,\n", " bootstrap=True,\n", " bootstrap_features=False,\n", " n_jobs=-1)\n", "\n", "# fit the data\n", "bag.fit(X_train, y_train)\n", "\n", "# make predictions\n", "preds_train = bag.predict(X_train)\n", "preds_test = bag.predict(X_test)" ], "metadata": { "id": "qofbJBBIUd7F" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "spearmanr = stats.spearmanr(a=preds_test, b=y_test, axis=0)\n", "print(spearmanr)" ], "metadata": { "id": "2GFmzulBUhQB", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "6e3b7128-216e-4dba-e430-42f426466de5" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "SignificanceResult(statistic=0.7144792120843033, pvalue=0.0)\n" ] } ] }, { "cell_type": "code", "source": [ "from sklearn.metrics import PredictionErrorDisplay\n", "fig, (ax0, ax1) = plt.subplots(figsize=(12, 6), nrows=1, ncols=2)\n", "\n", "# plot actual vs predicted values\n", "PredictionErrorDisplay.from_predictions(\n", " y_test,\n", " preds_test,\n", " ax=ax0,\n", " kind='actual_vs_predicted',\n", " scatter_kwargs={\"alpha\":0.5}\n", ")\n", "ax0.plot([], [], \" \", label=f\"Spearman r: {np.round(spearmanr.statistic, 4)}\")\n", "ax0.legend(loc=\"best\")\n", "ax0.axis('tight')\n", "\n", "PredictionErrorDisplay.from_predictions(\n", " y_test,\n", " preds_test,\n", " kind='residual_vs_predicted',\n", " ax=ax1,\n", " scatter_kwargs={\"alpha\":0.5}\n", ")\n", "\n", "ax1.plot([], [], \" \", label=f\"Spearman r: {np.round(spearmanr.statistic, 4)}\")\n", "ax1.legend(loc=\"best\")\n", "ax1.axis('tight')\n", "\n", "plt.tight_layout()\n", "plt.show();" ], "metadata": { "id": "9mH1iruEUrQb", "colab": { "base_uri": "https://localhost:8080/", "height": 546 }, "outputId": "5ee5ea2e-cf9c-484c-84b7-ff768cbebb4b" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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}, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "### Bagging Regressor on entire data" ], "metadata": { "id": "L59q0FuxK53R" } }, { "cell_type": "markdown", "source": [ "Final model for prediction on single and multiple mutants." ], "metadata": { "id": "asY5BVaLXhcN" } }, { "cell_type": "code", "source": [ "# define ridge model with RidgeSR alpha\n", "ridge = make_pipeline(StandardScaler(),\n", " Ridge(alpha=ridgsesr_alpha))\n", "\n", "# define bag model\n", "bag_emb_esm1v = BaggingRegressor(estimator=ridge,\n", " n_estimators=100,\n", " max_samples=1.0,\n", " max_features=1.0,\n", " bootstrap=True,\n", " bootstrap_features=False,\n", " oob_score=True,\n", " n_jobs=-1)\n", "\n", "# No train test split. Fit the bag model on entire dataset\n", "bag_emb_esm1v.fit(X, y)\n", "\n", "# make predictions\n", "preds_oob_esm1v = bag_emb_esm1v.oob_prediction_\n", "\n", "! mkdir -p models\n", "\n", "# save model\n", "with open('models/bag_emb_esm1v_rep7868aav2.pkl','wb') as f:\n", " pickle.dump(bag_emb_esm1v,f)" ], "metadata": { "id": "N0kWXe2xXSJo" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "print(stats.spearmanr(a=preds_oob_esm1v, b=y, axis=0))" ], "metadata": { "id": "5_ShzTcJYIGL", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "db6dda1b-d386-40b2-8c20-707d5d7efbdd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "SignificanceResult(statistic=0.7178772755854543, pvalue=0.0)\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Potential hits from the model" ], "metadata": { "id": "fa7moWUSLHFF" } }, { "cell_type": "markdown", "source": [ "The model is now ready for inference. We can now use the model to identify promising protein sequence variants for experimental validation." ], "metadata": { "id": "PvbZL74iIkul" } }, { "cell_type": "code", "source": [ "ds_with_preds = ds.add_column(\"predicted_label\", preds_oob_esm1v)\n", "ds_with_preds" ], "metadata": { "id": "CXhtHFe8Z0W0", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "bfc8eaf3-c189-461d-9e47-a768e799d91a" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Dataset({\n", " features: ['input_ids', 'attention_mask', 'labels', 'hidden_state', 'predicted_label'],\n", " num_rows: 11800\n", "})" ] }, "metadata": {}, "execution_count": 79 } ] }, { "cell_type": "code", "source": [ "# create a DataFrame with the input_ids, losses, and predicted/true labels:\n", "ds_with_preds.set_format(\"pandas\")\n", "cols = ['input_ids', 'labels', 'predicted_label']\n", "df_with_preds = ds_with_preds[:][cols]" ], "metadata": { "id": "ujZtDQNQZ8sy" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df_with_preds.head()" ], "metadata": { "id": "vG2SDdFVaI6T", "colab": { "base_uri": "https://localhost:8080/", "height": 206 }, "outputId": "3c15ac38-1931-480f-d4f2-e59386a12051" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " input_ids labels \\\n", "0 [0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 1.000000 \n", "1 [0, 5, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14,... 0.898696 \n", "2 [0, 23, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 0.680702 \n", "3 [0, 13, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 0.511988 \n", "4 [0, 9, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14,... 0.788339 \n", "\n", " predicted_label \n", "0 0.790839 \n", "1 0.714119 \n", "2 0.884781 \n", "3 0.806718 \n", "4 0.658499 " ], "text/html": [ "\n", "
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\n" ] }, "metadata": {}, "execution_count": 83 } ] }, { "cell_type": "code", "source": [ "from functools import partial\n", "def find_mutated_aa(best_seq, starter_seq):\n", " \"\"\"Return aminoacid substitution between two protein sequences of the same length\"\"\"\n", " mutated_aa = [starter_seq[i]+str(i+1)+best_seq[i] for i in range(len(starter_seq))\n", " if starter_seq[i] != best_seq[i]]\n", " return mutated_aa\n", "\n", "mut_in_rep78 = partial(find_mutated_aa, starter_seq=wt_seq)" ], "metadata": { "id": "u06NJ4iJaeAY" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "df_with_preds['mutated_aa'] = df_with_preds['sequence'].apply(mut_in_rep78)\n", "df_with_preds" ], "metadata": { "id": "P3NhRFzKabGY", "colab": { "base_uri": "https://localhost:8080/", "height": 597 }, "outputId": "0f4454e8-8ff2-48f5-f619-f78b2f9accf5" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " input_ids labels \\\n", "0 [0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 1.000000 \n", "1 [0, 5, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14,... 0.898696 \n", "2 [0, 23, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 0.680702 \n", "3 [0, 13, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 0.511988 \n", "4 [0, 9, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14,... 0.788339 \n", "... ... ... \n", "11795 [0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 1.154430 \n", "11796 [0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 0.994726 \n", "11797 [0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 1.065038 \n", "11798 [0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 0.826328 \n", "11799 [0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14... 0.834962 \n", "\n", " predicted_label sequence \\\n", "0 0.790839 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... \n", "1 0.714119 APGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... \n", "2 0.884781 CPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... \n", "3 0.806718 DPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... \n", "4 0.658499 EPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... \n", "... ... ... \n", "11795 0.825667 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... \n", "11796 0.816381 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... \n", "11797 0.903975 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... \n", "11798 0.917002 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... \n", "11799 0.959157 MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN... \n", "\n", " mutated_aa \n", "0 [] \n", "1 [M1A] \n", "2 [M1C] \n", "3 [M1D] \n", "4 [M1E] \n", "... ... \n", "11795 [Q621S] \n", "11796 [Q621T] \n", "11797 [Q621V] \n", "11798 [Q621W] \n", "11799 [Q621Y] \n", "\n", "[11800 rows x 5 columns]" ], "text/html": [ "\n", "
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input_idslabelspredicted_labelsequencemutated_aa
0[0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14...1.0000000.790839MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[]
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3[0, 13, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14...0.5119880.806718DPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[M1D]
4[0, 9, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14,...0.7883390.658499EPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[M1E]
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11795[0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14...1.1544300.825667MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[Q621S]
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input_idslabelspredicted_labelsequencemutated_aa
10072[0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14...0.8873481.522289MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[Q531C]
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9480[0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14...1.1417401.420908MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[D499W]
227[0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 22...0.7491801.418258MPGFYEIVIKVWSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[P12W]
9708[0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14...1.0646711.410387MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[S511W]
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2370[0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14...1.2719201.216066MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[E125R]
211[0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 23...1.8774071.215950MPGFYEIVIKVCSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[P12C]
986[0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14...0.9257741.215368MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKEWELPPDSDMDLN...[P52V]
662[0, 20, 14, 6, 18, 19, 9, 12, 7, 12, 15, 7, 14...1.1026181.214111MPGFYEIVIKVPSDLDGHLPGISDSFVNWVAEKESELPPDSDMDLN...[W35S]
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\n" ] }, "metadata": {}, "execution_count": 89 } ] }, { "cell_type": "code", "source": [ "df_with_preds.sort_values(by = ['predicted_label', 'labels'],\n", " ascending = [False, False]).to_csv('data/rep7868aav2_preds_emb_esm1v.csv')" ], "metadata": { "id": "SRBThO-VayOB" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "The model can additionally serve for prospective design by generating sequence proposals through in silico directed evolution. The exploration of in silico directed evolution will be delved into in a future notebook. In part two of this notebook, we will develop a complementary approach for sequence-to-function modeling on deep mutational scanning data: one relying fine-tuning pretrained protein langauge model." ], "metadata": { "id": "8Uz-S3s3piol" } }, { "cell_type": "markdown", "source": [ "## Please don’t fall into blind love with models' elegance and power" ], "metadata": { "id": "jPNDJJomlR4k" } }, { "cell_type": "markdown", "source": [ "I would like to end this notebook with an excerpt from Richard McElreath's book Statistical Rethinking about statistical models:\n", "\n", "> Scientists also make golems. Our golems rarely have physical form, but they too are often made of clay, living in silicon as computer code. These golems are scientific model. But these golems have real effects on the world, through the predictions they make and the intuitions they challenge or inspire. A concern with truth enlivens these models, but just like a golem or a modern robot, scientific models are neither true nor false, neither prophets nor charlatans. Rather they are constructs engineered for some purpose. These constructs are incredibly powerful, dutifully conducting their programmed calculations" ], "metadata": { "id": "BgZgjPNVc52-" } }, { "cell_type": "markdown", "source": [ "In the context of model inference, it is imperative to prioritize scientific rigor over statistical outcomes. A thorough comprehension of the data generation process, acknowledgment of model limitations, and an understanding of the distribution within the inference regime are essential. Consideration of specific experimental details is paramount:\n", "\n", "1) **Assay fidelity vs Assay throughput**:\n", "One practical application of the [DMS data](https://raw.githubusercontent.com/churchlab/aav_rep_scan/master/analysis/selection_values/wtaav2_selection_values_barcode.csv) from [rep mutagenesis scan paper published in 2023 from George M. Church lab](https://doi.org/10.7554/eLife.87730.1) is to improve recombinant adeno-associated virus (rAAV) production for therapeutic purposes. rAAV production is carefully measured using viral genome titer assay (high fidelity, low throughput assay). Deep mutational scanning experiments use a high-throughput assay to screen an initial variant library and isolate variants with a desired functional property. The initial library and the isolated variants are sequenced, and a fitness score is computed for each variant based on the frequency of reads in both sets. The fitness score from low fidelity high throughput assay are only a proxy measurement for the viral genome titer we want to optimize for.\n", "\n", "2) **Role of Data Quality in Learning Accurate Sequence-function Models**:\n", "The accuracy of the computed fitness scores hinges on the sensitivity and specificity of the high-throughput assay, the number of times each variant was characterized in the high-throughput assay, and the number of DNA sequencing reads per variant. In cases where any of these factors is insufficient, the resultant fitness scores may not accurately represent the genuine fitness values of the characterized proteins. This discrepancy complicates the task of training a model to grasp the underlying sequence-to-function mapping. Hence, practical considerations such as the size and quality of the DMS dataset could influence protein-specific performance.\n", "\n", "3) **Design of the study**:\n", "The paper could have additionally enriched for high fitness variants by performing transduction with isolated AAV particles.\n", "\n", "4) **Sequence-to-function modeling complexity**:\n", "The protein sequence-to-function model faces complexity due to the necessity to capture non-additive effects (epistasis) and the challenge of generalizing to unseen mutations, spanning from low-order to higher-order mutations." ], "metadata": { "id": "L-8jea-9bLEp" } }, { "cell_type": "code", "source": [ "# Download model\n", "from google.colab import files\n", "files.download(\"models/bag_emb_esm1v_rep7868aav2.pkl\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, "id": "0s80FhlLz5Uh", "outputId": "517c2704-63a6-41db-80b9-e6d20d1007aa" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_e7f38038-dad7-4fd8-9f9c-e3add2994b77\", \"bag_emb_esm1v_rep7868aav2.pkl\", 4762664)" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "!md5sum models/bag_emb_esm1v_rep7868aav2.pkl" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "L3aIvsPJ1yJr", "outputId": "c8827d46-47c2-42f2-d1c9-e676baf1eb88" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "b2adfa4cf9be0b8614ab2b0c6aeee622 models/bag_emb_esm1v_rep7868aav2.pkl\n" ] } ] }, { "cell_type": "code", "source": [ "# download all files from colab data folder\n", "!zip -r /content/data.zip /content/data\n", "files.download('/content/data.zip')\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 104 }, "id": "N8NhEQFv2_Ia", "outputId": "1bdb3b31-ad67-4017-b6c7-2d4596b7fd27" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "updating: content/data/ (stored 0%)\n", "updating: content/data/rep7868aav2_emb_esm1v.pkl (deflated 42%)\n", "updating: content/data/fitness_by_mutation_rep7868aav2.pkl (deflated 84%)\n", "updating: content/data/rep7868aav2_preds_emb_esm1v.csv (deflated 98%)\n", "updating: content/data/.ipynb_checkpoints/ (stored 0%)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_b95f9c24-e122-4c39-817e-4e85cd641cf3\", \"data.zip\", 115688424)" ] }, "metadata": {} } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "XjiYVfLE3OYa" }, "execution_count": null, "outputs": [] } ] }