{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A comparison to the below\n",
"\n",
"https://github.com/tirthajyoti/Interactive_Machine_Learning/blob/master/Interactive%20ML-Regression.ipynb"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from reactpy import Reactive, Interact, Plot\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib widget\n",
"\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"from sklearn.linear_model import LassoCV\n",
"from sklearn.linear_model import RidgeCV\n",
"from sklearn.linear_model import LinearRegression\n",
"from sklearn.pipeline import make_pipeline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"r = Reactive(lazy_eval=False)\n",
"r.update(get_ipython().user_ns)\n",
"get_ipython().user_ns = r"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"model_id": "806090b4dcef4f64a7c07d600ecd105d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(Dropdown(description='value', options={'Low (50 samples)': 50, 'High (200 samples)': 200…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
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"model_id": "6452718ba88e4be7a1f174bc7bcf3616",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=-3, description='value', max=0, min=-5), Output()), _dom_classes=('widge…"
]
},
"metadata": {},
"output_type": "display_data"
},
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"data": {
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"model_id": "48800e7e8ca14fbaa4dc7a09fa1c1c1e",
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"version_minor": 0
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"text/plain": [
"interactive(children=(IntSlider(value=2, description='value', max=5), Output()), _dom_classes=('widget-interac…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"interactive(children=(IntSlider(value=2, description='value', max=5), Output()), _dom_classes=('widget-interac…"
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"interactive(children=(FloatSlider(value=0.5, description='value', max=1.0, min=0.1), Output()), _dom_classes=(…"
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"metadata": {},
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"interactive(children=(FloatSlider(value=0.0, description='value', max=2.0, min=-2.0, step=0.5), Output()), _do…"
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},
"metadata": {},
"output_type": "display_data"
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{
"data": {
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"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"N_samples = Interact('Samples', {'Low (50 samples)':50,'High (200 samples)':200})\n",
"x_min = Interact('Min X', (-5,0,1))\n",
"x_max = Interact('Max X', (0,5,1))\n",
"noise_mag = Interact('Mag Noise', (0,5,1))\n",
"noise_sd = Interact('Std. Noise', (0.1,1,0.1))\n",
"noise_mean = Interact('Mean Noise', (-2,2,0.5))\n",
"\n",
"x1= r(lambda x_min, x_max, N_samples: np.linspace(x_min,x_max,N_samples*5))\n",
"x = r(lambda x1, N_samples: np.random.choice(x1, size=N_samples))\n",
"y = r(lambda x: 2*x-0.6*x**2+0.2*x**3+18*np.sin(x))\n",
"y1 = r(lambda x1: 2*x1-0.6*x1**2+0.2*x1**3+18*np.sin(x1))\n",
"yp = r(lambda y, noise_mag, noise_mean, noise_sd, N_samples: y+noise_mag*np.random.normal(\n",
" loc=noise_mean,scale=noise_sd,size=N_samples))\n",
"\n",
"def plot(ax,x,yp,x1,y1):\n",
" ax.plot(x1,y1,c='k',lw=2)\n",
" ax.scatter(x,yp,edgecolors='k',c='yellow',s=60)\n",
" ax.grid(True) \n",
"\n",
"p = Plot(plot)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"lasso_eps = 0.01\n",
"lasso_nalpha=20\n",
"lasso_iter=3000\n",
"ridge_alphas = (0.001,0.01,0.1,1)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
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"interactive(children=(IntSlider(value=50, description='value', max=99, min=10, step=5), Output()), _dom_classe…"
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"interactive(children=(IntSlider(value=5, description='value', max=10, min=1), Output()), _dom_classes=('widget…"
]
},
"metadata": {},
"output_type": "display_data"
},
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"data": {
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"version_minor": 0
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"Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"model_type = Interact('Model', ['Linear regression','LASSO with CV', 'Ridge with CV'])\n",
"test_size = Interact('Test Size %', (10,99,5))\n",
"degree = Interact('Degree', (1,10,1))\n",
"\n",
"split = r(lambda x,y,test_size: train_test_split(\n",
" x,y,test_size=test_size*0.01,random_state=55))\n",
"X_train = r(lambda split: split[0].reshape(-1,1))\n",
"X_test = r(lambda split: split[1].reshape(-1,1))\n",
"y_train = r(lambda split: split[2])\n",
"y_test = r(lambda split: split[3])\n",
"\n",
"def get_model(X_train, y_train, model_type, degree, ):\n",
" if (model_type=='Linear regression'):\n",
" model = make_pipeline(PolynomialFeatures(degree,interaction_only=False), \n",
" LinearRegression(normalize=True))\n",
" if (model_type=='LASSO with CV'): \n",
" model = make_pipeline(PolynomialFeatures(degree,interaction_only=False), \n",
" LassoCV(eps=lasso_eps,n_alphas=lasso_nalpha,\n",
" max_iter=lasso_iter,normalize=True,cv=5)) \n",
" if (model_type=='Ridge with CV'): \n",
" model = make_pipeline(PolynomialFeatures(degree,interaction_only=False), \n",
" RidgeCV(alphas=ridge_alphas,normalize=True,cv=5))\n",
" model.fit(X_train,y_train)\n",
" return model\n",
"\n",
"model = r(get_model)\n",
"train_pred = r(lambda model, X_train: model.predict(X_train))\n",
"train_score = r(lambda model, X_train, y_train: model.score(X_train,y_train))\n",
"test_pred = r(lambda model, X_test: model.predict(X_test))\n",
"test_score = r(lambda model, X_test, y_test: model.score(X_test,y_test))\n",
"RMSE_test = r(lambda test_pred, y_test: np.sqrt(np.mean(np.square(test_pred-y_test))))\n",
"RMSE_train = r(lambda train_pred, y_train: np.sqrt(np.mean(np.square(train_pred-y_train))))\n",
"\n",
"fig,(ax1, ax2) = plt.subplots(1, 2, figsize=(10,6))\n",
"ax1.set_xlim(-3,3)\n",
"ax1.set_ylim(-30,30)\n",
"ax2.set_xlim(-3,3)\n",
"ax2.set_ylim(-30,30)\n",
"\n",
"def plot1(ax, test_score, X_test, y_test, test_pred):\n",
" ax.set_title(\"Test set performance\\nTest score: %.3f\"%(test_score),fontsize=16)\n",
" ax.set_xlabel(\"X-test\",fontsize=13)\n",
" ax.set_ylabel(\"y-test\",fontsize=13)\n",
" ax.scatter(X_test,y_test,edgecolors='k',c='blue',s=60)\n",
" ax.scatter(X_test,test_pred,edgecolors='k',c='yellow',s=60)\n",
" ax.grid(True)\n",
" ax.legend(['Actual test values','Predicted values'])\n",
" \n",
"def plot2(ax, train_score, X_train, y_train, train_pred):\n",
" ax.set_title(\"Training set performance\\nTraining score: %.3f\"%(train_score),fontsize=16)\n",
" ax.set_xlabel(\"X-train\",fontsize=13)\n",
" ax.set_ylabel(\"y-train\",fontsize=13)\n",
" ax.scatter(X_train,y_train,c='blue')\n",
" ax.scatter(X_train,train_pred,c='yellow')\n",
" ax.grid(True)\n",
" ax.legend(['Actual training values','Fitted values'])\n",
" \n",
"p2 = Plot(plot1, ax=ax1)\n",
"p3 = Plot(plot2, ax=ax2)\n"
]
},
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"cell_type": "code",
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