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anoopkcn/tricks_and_tips_of_ml_trade.ipynb

Created November 17, 2021 13:09
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tricks_and_tips_of_ml_trade.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np \n",
"import matplotlib.pyplot as plt "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature selector that removes all low variance feature "
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_selection import VarianceThreshold"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[2, 0, 2],\n",
" [1, 4, 3],\n",
" [1, 1, 3]])"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x=[[0,2,0,2],[0,1,4,3],[0,1,1,3]]\n",
"selector = VarianceThreshold(threshold=0.0)\n",
"selector.fit_transform(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imputation of missing values"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[7. , 3. , 4. ],\n",
" [3. , 1. , 5.5],\n",
" [4. , 5. , 7. ]])"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.impute import SimpleImputer\n",
"\n",
"x=[[7,3,4],[3,1,np.nan],[4,5,7]]\n",
"imp_mean = SimpleImputer(missing_values=np.nan, strategy='median')\n",
"imp_mean.fit_transform(x)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Apply power transformation to make data more gausian like"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import PowerTransformer\n",
"\n",
"rng = np.random.RandomState(0)\n",
"X=np.sort(rng.chisquare(4,100),axis =0).reshape(-1,1)\n",
"pt=PowerTransformer(method='yeo-johnson',standardize=True)\n",
"Xt=pt.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
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"text/plain": [
"<Figure size 720x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,4))\n",
"fig.tight_layout()\n",
"ax1.hist(X);\n",
"ax2.hist(Xt);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reduce the impact of outliers using quantiles information"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import QuantileTransformer \n",
"rng = np.random.RandomState(0)\n",
"X=np.sort(rng.chisquare(4,100),axis =0).reshape(-1,1)\n",
"qt=QuantileTransformer(n_quantiles=10,output_distribution='normal',random_state=0)\n",
"Xt=qt.fit_transform(X)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 720x288 with 2 Axes>"
]
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}
],
"source": [
"fig,(ax1,ax2)=plt.subplots(1,2,figsize=(10,4))\n",
"fig.tight_layout()\n",
"ax1.hist(X);\n",
"ax2.hist(Xt);"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Transform by removing the mean and scaling to unit variance "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0. , -1.41421356, 0.46291005],\n",
" [-1.22474487, 0.70710678, 0.9258201 ],\n",
" [ 1.22474487, 0.70710678, -1.38873015]])"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"X = [[1,-2,2],[-2,1,3],[4,1,-2]]\n",
"sc = StandardScaler()\n",
"sc.fit_transform(X)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Remove the median and scales the data to interquartile range(IQR) "
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0. , -2. , 0. ],\n",
" [-1. , 0. , 0.4],\n",
" [ 1. , 0. , -1.6]])"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.preprocessing import RobustScaler\n",
"\n",
"X = [[1,-2,2],[-2,1,3],[4,1,-2]]\n",
"rc = RobustScaler()\n",
"rc.fit_transform(X)\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"x= np.linspace(-np.pi, np.pi,100)\n",
"y = np.sin(x)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x12ff17fa0>]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 432x288 with 1 Axes>"
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