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December 30, 2018 08:38
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Data Preprocessing Project - Feature Scaling
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"# Data Preprocessing Project – Feature Scaling\n", | |
"\n", | |
"\n", | |
"In this project, I discuss various data preprocessing techniques related to feature scaling.\n", | |
"\n", | |
"\n", | |
"The contents of this project are divided as follows:-\n", | |
"\n", | |
"\n", | |
"\n", | |
"## Table of Contents\n", | |
"\n", | |
"\n", | |
"1.\tIntroduction\n", | |
"\n", | |
"2.\tRescaling data with MinMaxScaler\n", | |
"\n", | |
"3.\tStandardising data with StandardScaler\n", | |
"\n", | |
"4. Rescaling data with MaxAbsScaler\n", | |
"\n", | |
"5. Rescaling data with RobustScaler\n", | |
"\n", | |
"6.\tNormalizing data with Normalizer\n", | |
"\n", | |
"7.\tBinarizing data with Binarizer\n", | |
"\n", | |
"8.\tMean removal with scale\n", | |
"\n", | |
"\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 1. Introduction\n", | |
"\n", | |
"\n", | |
"One of the most important data preprocessing step, we need to apply to our data is feature scaling. When we encounter any real world data set, the independent or feature variables may be mapped onto different scales. **Feature scaling** refers to procedures used to standardize these independent or feature variables so that they are mapped onto same scales.\n", | |
"\n", | |
"\n", | |
"Most of the ML algorithms perform well when the feature variables are mapped onto the same scale. They don’t perform well when features are mapped onto different scales. For example, in stochastic gradient descent, feature scaling can improve the convergence speed of the algorithm. In support vector machines, it can reduce the time to find support vectors. \n", | |
"\n", | |
"\n", | |
"But, there are few exceptions as well. Decision trees and random forests are two of the algorithms where we don’t need to worry about feature scaling. These algorithms are scale invariant. Similarly, Naive Bayes and Linear Discriminant Analysis are not affected by feature scaling. In Short, any Algorithm which is not distance based is not affected by feature scaling.\n", | |
"\n", | |
"So, let’s start our discussion of various techniques associated with feature scaling.\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Import the dependencies\n", | |
"\n", | |
"import numpy as np\n", | |
"import pandas as pd\n", | |
"import matplotlib\n", | |
"import matplotlib.pyplot as plt\n", | |
"%matplotlib inline\n", | |
"matplotlib.style.use('ggplot')\n", | |
"import seaborn as sns" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# ignore the warnings\n", | |
"\n", | |
"import warnings\n", | |
"warnings.simplefilter(action = \"ignore\", category = FutureWarning)\n", | |
"warnings.simplefilter(action = \"ignore\", category = RuntimeWarning)" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 2. Rescaling data with MinMaxScaler\n", | |
"\n", | |
"\n", | |
"This technique of rescaling is also called **min-max scaling** or **min-max normalization**. **Normalization** refers to the rescaling of the features to a range of [0, 1], which is a special case of min-max scaling. So, in this technique, values are shifted and rescaled so that they end up ranging from zero to one. We do this by subtracting the minimum value (xmin ) and dividing by the maximum value (xmax ) minus the minimum value (xmin ). \n", | |
"\n", | |
"\n", | |
"Mathematically, the new value x(i)norm of a sample x(i) can be calculated as follows:-\n", | |
"\n", | |
" \n", | |
"\t\t x(i)norm = (xi- xmin )/(xmax- xmin )\n", | |
" \n", | |
"\t\n", | |
"Here, x(i) is a particular sample value. xmax and xmin is the maximum and minimum feature value in a column.\n", | |
"\n", | |
"\t\n", | |
"Scikit-Learn provides a transformer called **MinMaxScaler** for this task. It has a feature range parameter to adjust the range of values. This estimator fit and transform each feature variable individually such that it is in the given range (between zero and one) on the training set. \n", | |
"\n", | |
"**MinMaxScaler** works well for the cases when the distribution is not normal or when the standard deviation is very small. But, it has one drawback. It is sensitive to outliers\n", | |
"\n", | |
"\n", | |
"The syntax for implementing min-max scaling procedure in Scikit-Learn is given as follows:- \n", | |
"\n", | |
"\n", | |
"`from sklearn.preprocessing import MinMaxScaler`\n", | |
"\n", | |
"`ms = MinMaxScaler()`\n", | |
"\n", | |
"`X_train_ms = ms.fit_transform(X_train)`\n", | |
"\n", | |
"`X_test_ms = ms.transform(X_test)`\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Create sample dataset to demonstrate minmaxscaling\n", | |
"\n", | |
"\n", | |
"df1 = pd.DataFrame({\n", | |
" # positive skew\n", | |
" 'x1': np.random.chisquare(10, 1000),\n", | |
" # negative skew \n", | |
" 'x2': np.random.beta(10, 2, 1000) * 40,\n", | |
" # no skew\n", | |
" 'x3': np.random.normal(50, 3, 1000)\n", | |
"})" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Use MinMaxScaler to apply minmaxscaling\n", | |
"\n", | |
"from sklearn.preprocessing import MinMaxScaler\n", | |
"ms = MinMaxScaler()\n", | |
"scaled_df1 = ms.fit_transform(df1)\n", | |
"scaled_df1 = pd.DataFrame(scaled_df1, columns=['x1', 'x2', 'x3'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
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\n", | |
"text/plain": [ | |
"<Figure size 720x576 with 2 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Plot and visualize the results\n", | |
"\n", | |
"\n", | |
"fig,(ax1, ax2) = plt.subplots(ncols = 2, figsize = (10,8))\n", | |
"\n", | |
"ax1.set_title('Before min-max scaling')\n", | |
"sns.kdeplot(df1['x1'], ax=ax1)\n", | |
"sns.kdeplot(df1['x2'], ax=ax1)\n", | |
"sns.kdeplot(df1['x3'], ax=ax1)\n", | |
"\n", | |
"ax2.set_title('After min-max scaling')\n", | |
"sns.kdeplot(scaled_df1['x1'], ax=ax2)\n", | |
"sns.kdeplot(scaled_df1['x2'], ax=ax2)\n", | |
"sns.kdeplot(scaled_df1['x3'], ax=ax2)\n", | |
"\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"**Interpretation**\n", | |
"\n", | |
"We can see that the skewness of the distributions are maintained. But, now the three distributions are brought onto the same scale such that they overlap." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"As with all the other transformers, we fit this transformer to the training data only, not to the full data set (including the test set). Only then we can use them to transform the training set and the test set and new data." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 3. Standardising data with StandardScaler\n", | |
"\n", | |
"\n", | |
"There is another practical approach for feature scaling which might be more useful in certain circumstances. It is called **standardization**. It can be more useful for many machine learning algorithms, especially for optimization algorithms such as gradient descent.\n", | |
"\n", | |
"\n", | |
"In **standardization**, first we determine the distribution mean and standard deviation for each feature. Next we subtract the mean from each feature. Then we divide the values of each feature by its standard deviation. So, in standardization, we center the feature columns at mean 0 with standard deviation 1 so that the feature columns takes the form of a normal distribution, which makes it easier to learn the weights.\n", | |
"\n", | |
"\n", | |
"Scikit-Learn provides a transformer called **StandardScaler** for standardization. The **StandardScaler** transformer assumes that the data is normally distributed within each feature and it will scale them such that the distribution is centered around 0 and and have a standard deviation of 1.\n", | |
"\n", | |
"\n", | |
"Mathematically, **standardization** can be expressed by the following equation: \n", | |
"\n", | |
"\n", | |
"\t\tx(i)std = ( x(i)- μx)/(σx )\n", | |
"\n", | |
"\n", | |
"Here, x(i) is a particular sample value and x(i)std is its standard deviation , μx is the sample mean of a particular feature column and σx is the corresponding standard deviation.\n", | |
"\n", | |
"\n", | |
"Min-max scaling scales the data to a limited range of values. Unlike min-max scaling, standardization does not bound values to a specific range. So, standardization is much less affected by outliers. Standardization maintains useful information about outliers and is much less affected by them. It makes the algorithm less sensitive to outliers in contrast to min-max scaling. \n", | |
"\n", | |
"\n", | |
"The syntax to implement standardization is quite similar to min-max scaling given as follows :-\n", | |
"\n", | |
"\n", | |
"`from sklearn.preprocessing import StandardScaler`\n", | |
"\n", | |
"`ss = StandardScaler()`\n", | |
"\n", | |
"`X_train_ss = ss.fit_transform(X_train)`\n", | |
"\n", | |
"`X_test_ss = ss.transform(X_test)`\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Create sample dataset to demonstrate standardization\n", | |
"\n", | |
"np.random.seed(1)\n", | |
"df2 = pd.DataFrame({\n", | |
" 'x1':np.random.normal(0, 10, 10000),\n", | |
" 'x2':np.random.normal(10, 20, 10000),\n", | |
" 'x3':np.random.normal(-10, 10, 10000)\n", | |
"})" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 7, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Use StandardScaler to apply Standardisation\n", | |
"\n", | |
"from sklearn.preprocessing import StandardScaler\n", | |
"ss = StandardScaler()\n", | |
"scaled_df2 = ss.fit_transform(df2)\n", | |
"scaled_df2 = pd.DataFrame(scaled_df2, columns = ['x1', 'x2', 'x3'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 8, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 720x576 with 2 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Plot and visualize the results\n", | |
"\n", | |
"\n", | |
"fig,(ax1, ax2) = plt.subplots(ncols = 2, figsize = (10,8))\n", | |
"\n", | |
"ax1.set_title('Before standardization')\n", | |
"sns.kdeplot(df2['x1'], ax=ax1)\n", | |
"sns.kdeplot(df2['x2'], ax=ax1)\n", | |
"sns.kdeplot(df2['x3'], ax=ax1)\n", | |
"\n", | |
"ax2.set_title('After standardization')\n", | |
"sns.kdeplot(scaled_df2['x1'], ax=ax2)\n", | |
"sns.kdeplot(scaled_df2['x2'], ax=ax2)\n", | |
"sns.kdeplot(scaled_df2['x3'], ax=ax2)\n", | |
"\n", | |
"plt.show()\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"**Interpretation**\n", | |
"\n", | |
"As we can see, all the features are now on the same scale relative to one another. \n", | |
"\n", | |
"We should fit the **StandardScaler** class only once on the training data set and use those parameters to transform the test set or new data set. \n", | |
"\n", | |
"So, we can **standardize** the training dataset and use the same mean and standard deviation to **standardize** the test dataset.\n" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 4. Rescaling data with MaxAbsScaler\n", | |
"\n", | |
"\n", | |
"\n", | |
"In this feature rescaling task, we rescale each feature by its maximum absolute value. So, the maximum absolute value of each feature in the training set will be 1.0. It does not affect the data and hence there is no effect on sparsity.\n", | |
"\n", | |
"Scikit-Learn provides **MaxAbsScaler** transformer for this task.\n", | |
"\n", | |
"The syntax for implementing max-abs scaling procedure in Scikit-Learn is given as follows:- \n", | |
"\n", | |
"\n", | |
"`from sklearn.preprocessing import MaxAbsScaler`\n", | |
"\n", | |
"`mabs = MinMaxScaler()`\n", | |
"\n", | |
"`X_train_mabs = mabs.fit_transform(X_train)`\n", | |
"\n", | |
"`X_test_mabs = mabs.transform(X_test)`\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 9, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Create sample dataset to demonstrate maxabsscaling\n", | |
"\n", | |
"\n", | |
"df3 = pd.DataFrame({\n", | |
" # positive skew\n", | |
" 'x1': np.random.chisquare(10, 1000),\n", | |
" # negative skew \n", | |
" 'x2': np.random.beta(10, 2, 1000) * 40,\n", | |
" # no skew\n", | |
" 'x3': np.random.normal(50, 3, 1000)\n", | |
"})" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 10, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Use MaxAbsScaler to apply maxabsscaling\n", | |
"\n", | |
"from sklearn.preprocessing import MaxAbsScaler\n", | |
"mabs = MaxAbsScaler()\n", | |
"scaled_df3 = mabs.fit_transform(df3)\n", | |
"scaled_df3 = pd.DataFrame(scaled_df3, columns=['x1', 'x2', 'x3'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 11, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 720x576 with 2 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Plot and visualize the results\n", | |
"\n", | |
"\n", | |
"fig,(ax1, ax2) = plt.subplots(ncols = 2, figsize = (10,8))\n", | |
"\n", | |
"ax1.set_title('Before max-abs scaling')\n", | |
"sns.kdeplot(df3['x1'], ax=ax1)\n", | |
"sns.kdeplot(df3['x2'], ax=ax1)\n", | |
"sns.kdeplot(df3['x3'], ax=ax1)\n", | |
"\n", | |
"ax2.set_title('After max-abs scaling')\n", | |
"sns.kdeplot(scaled_df3['x1'], ax=ax2)\n", | |
"sns.kdeplot(scaled_df3['x2'], ax=ax2)\n", | |
"sns.kdeplot(scaled_df3['x3'], ax=ax2)\n", | |
"\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"**Interpretation**\n", | |
"\n", | |
"**MaxAbsScaler** results in the feature variables being rescaled by their maximum absolute value." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 5. Rescaling using RobustScaler\n", | |
"\n", | |
"\n", | |
"\n", | |
"**StandardScaler** can often give misleading results when the data contain outliers. Outliers can often influence the sample mean and variance and hence give misleading results. In such cases, it is better to use a scalar that is robust against outliers. Scikit-Learn provides a transformer called **RobustScaler** for this purpose.\n", | |
"\n", | |
"\n", | |
"The **RobustScaler** is very similar to **MinMaxScaler**. The difference lies in the parameters used for scaling. \n", | |
"While **MinMaxScaler** uses minimum and maximum values for rescaling, **RobustScaler** uses interquartile(IQR) range \n", | |
"for the same.\n", | |
"\n", | |
"\n", | |
"\n", | |
"Mathematically, the new value x(i)norm of a sample x(i) can be calculated as follows:-\n", | |
"\n", | |
" \n", | |
"\t\t x(i) = (xi- Q1(x) )/(Q3(x) - Q1(x))\n", | |
" \n", | |
"\t\n", | |
"Here, x(i) is the scaled value, xi is a particular sample value, Q1(x) and Q3(x) are the 1st quartile (25th quantile) and \n", | |
"3rd quartile (75th quantile) respectively. So, Q3(x) - Q1(x) is the difference between 3rd quartile (75th quantile) and \n", | |
"1st quartile (25th quantile) respectively. It is called IQR (Interquartile Range).\n", | |
"\n", | |
"\t\n", | |
"\n", | |
"The syntax for implementing scaling using RobustScaler in Scikit-Learn is given as follows:- \n", | |
"\n", | |
"\n", | |
"`from sklearn.preprocessing import RobustScaler`\n", | |
"\n", | |
"`rb = RobustScaler()`\n", | |
"\n", | |
"`X_train_rb = rb.fit_transform(X_train)`\n", | |
"\n", | |
"`X_test_rb = rb.transform(X_test)`\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 12, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Create sample dataset to demonstrate scaling using RobustScaler\n", | |
"\n", | |
"df4 = pd.DataFrame({\n", | |
" # Distribution with lower outliers\n", | |
" 'x1': np.concatenate([np.random.normal(20, 1, 1000), np.random.normal(1, 1, 25)]),\n", | |
" # Distribution with higher outliers\n", | |
" 'x2': np.concatenate([np.random.normal(30, 1, 1000), np.random.normal(50, 1, 25)]),\n", | |
"})" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 13, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Use RobustScaler for scaling\n", | |
"\n", | |
"from sklearn.preprocessing import RobustScaler\n", | |
"rb = RobustScaler()\n", | |
"robust_scaled_df4 = rb.fit_transform(df4)\n", | |
"robust_scaled_df4 = pd.DataFrame(robust_scaled_df4, columns=['x1', 'x2'])\n", | |
"\n", | |
"# Use MinMaxScaler for Normalization\n", | |
"from sklearn.preprocessing import MinMaxScaler\n", | |
"ms = MinMaxScaler()\n", | |
"minmax_scaled_df4 = ms.fit_transform(df4)\n", | |
"minmax_scaled_df4 = pd.DataFrame(minmax_scaled_df4, columns=['x1', 'x2'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 14, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 720x576 with 3 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Plot and visualize the results\n", | |
"\n", | |
"fig, (ax1, ax2, ax3) = plt.subplots(ncols=3, figsize=(10, 8))\n", | |
"\n", | |
"ax1.set_title('Before Scaling')\n", | |
"sns.kdeplot(df4['x1'], ax=ax1)\n", | |
"sns.kdeplot(df4['x2'], ax=ax1)\n", | |
"\n", | |
"ax2.set_title('After Robust Scaling')\n", | |
"sns.kdeplot(robust_scaled_df4['x1'], ax=ax2)\n", | |
"sns.kdeplot(robust_scaled_df4['x2'], ax=ax2)\n", | |
"\n", | |
"ax3.set_title('After Min-Max Scaling')\n", | |
"sns.kdeplot(minmax_scaled_df4['x1'], ax=ax3)\n", | |
"sns.kdeplot(minmax_scaled_df4['x2'], ax=ax3)\n", | |
"\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"**Interpretation**\n", | |
"\n", | |
"We can see that, **RobustScaler** transform the distributions to brought them onto the same scale.The distributions actually overlap each other. The outliers remain outside the range of the new distributions. \n", | |
"\n", | |
"In **MinMaxScaler**, the two normal distributions are kept separate by the outliers that are inside the range of 0 and 1." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 6. Normalizing data with Normalizer\n", | |
"\n", | |
"\n", | |
"In this feature scaling task, we rescale each observation to a length of 1 (a unit norm). Scikit-Learn provides, the **Normalizer** class for this task. In this task, we scale the components of a feature vector such that the complete vector \n", | |
"has length one. \n", | |
"\n", | |
"\n", | |
"This usually means dividing each component by the Euclidean length (magnitude) of the vector.\n", | |
"\n", | |
"\n", | |
"Mathematically, **normalization** can be expressed by the following equation: \n", | |
"\n", | |
"\n", | |
"`x(i)norm = x(i) / | x(i)|`\n", | |
"\n", | |
"\n", | |
"where x(i) is a particular sample value , x(i)norm is its normalized value and | x(i)| is the corresponding Euclidean length of the vector. \n", | |
"\n", | |
"\n", | |
"The syntax for normalization is quite similar to standardization given as follows:-\n", | |
"\n", | |
"\n", | |
"`from sklearn.preprocessing import Normalizer`\n", | |
"\n", | |
"`norm = Normalizer ()`\n", | |
"\n", | |
"`X_train_norm = norm.fit_transform(X_train)`\n", | |
"\n", | |
"`X_test_norm = norm.transform(X_test)`\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 15, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"from mpl_toolkits.mplot3d import Axes3D" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 16, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"df5 = pd.DataFrame({\n", | |
" 'x1': np.random.randint(-100, 100, 1000).astype(float),\n", | |
" 'y1': np.random.randint(-80, 80, 1000).astype(float),\n", | |
" 'z1': np.random.randint(-150, 150, 1000).astype(float),\n", | |
"})" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 17, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"\n", | |
"from sklearn.preprocessing import Normalizer\n", | |
"nm = Normalizer()\n", | |
"scaled_df5 = nm.fit_transform(df5)\n", | |
"scaled_df5 = pd.DataFrame(scaled_df5, columns= ['x1', 'y1', 'z1'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 18, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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\n", | |
"text/plain": [ | |
"<Figure size 648x360 with 2 Axes>" | |
] | |
}, | |
"metadata": { | |
"needs_background": "light" | |
}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"fig = plt.figure(figsize=(9, 5))\n", | |
"\n", | |
"ax1 = fig.add_subplot(121, projection='3d')\n", | |
"ax2 = fig.add_subplot(122, projection='3d')\n", | |
"\n", | |
"ax1.scatter(df5['x1'], df5['y1'], df5['z1'])\n", | |
"ax2.scatter(scaled_df5['x1'], scaled_df5['y1'], scaled_df5['z1'])\n", | |
"\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 7. Binarizing data with Binarizer\n", | |
"\n", | |
"\n", | |
"In this feature scaling procedure, we binarize the data (set feature values equal to 0 or 1) according to a threshold. \n", | |
"So, using a binary threshold, we transform our data by marking the values above it to 1 and those equal to or below it to 0. \n", | |
"\n", | |
"\n", | |
"Scikit-Learn provides **Binarizer** class for this purpose. The syntax for binarizing the data follow the same rules as above and is given below:-\n", | |
"\n", | |
"\n", | |
"`from sklearn.preprocessing import Binarizer`\n", | |
"\n", | |
"`bin = Binarizer ()`\n", | |
"\n", | |
"`X_train_bin = bin.fit_transform(X_train)`\n", | |
"\n", | |
"`X_test_bin = bin.transform(X_test)`\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 19, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Create sample dataset to demonstrate binarization \n", | |
"\n", | |
"data1 = [[2, -2, 1],\n", | |
" [5, -5, 3],\n", | |
" [1, 0, -1]]\n", | |
"\n", | |
"df6 = pd.DataFrame(data1, columns = ['x1', 'x2', 'x3'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 20, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Use Binarizer to apply binarization\n", | |
"\n", | |
"from sklearn.preprocessing import Binarizer\n", | |
"binr = Binarizer(threshold=0.0, copy=False)\n", | |
"scaled_df6 = binr.fit_transform(df6)\n", | |
"scaled_df6 = pd.DataFrame(scaled_df6, columns = ['x1', 'x2', 'x3'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 21, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/html": [ | |
"<div>\n", | |
"<style scoped>\n", | |
" .dataframe tbody tr th:only-of-type {\n", | |
" vertical-align: middle;\n", | |
" }\n", | |
"\n", | |
" .dataframe tbody tr th {\n", | |
" vertical-align: top;\n", | |
" }\n", | |
"\n", | |
" .dataframe thead th {\n", | |
" text-align: right;\n", | |
" }\n", | |
"</style>\n", | |
"<table border=\"1\" class=\"dataframe\">\n", | |
" <thead>\n", | |
" <tr style=\"text-align: right;\">\n", | |
" <th></th>\n", | |
" <th>x1</th>\n", | |
" <th>x2</th>\n", | |
" <th>x3</th>\n", | |
" </tr>\n", | |
" </thead>\n", | |
" <tbody>\n", | |
" <tr>\n", | |
" <th>0</th>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>1</th>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>1</td>\n", | |
" </tr>\n", | |
" <tr>\n", | |
" <th>2</th>\n", | |
" <td>1</td>\n", | |
" <td>0</td>\n", | |
" <td>0</td>\n", | |
" </tr>\n", | |
" </tbody>\n", | |
"</table>\n", | |
"</div>" | |
], | |
"text/plain": [ | |
" x1 x2 x3\n", | |
"0 1 0 1\n", | |
"1 1 0 1\n", | |
"2 1 0 0" | |
] | |
}, | |
"execution_count": 21, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# Print the results\n", | |
"\n", | |
"scaled_df6" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"**Interpretation**\n", | |
"\n", | |
"We can see that the dataframe df6 values are converted into binary values of 0 or 1 according to the threshold of 0." | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"## 8. Mean removal with scale\n", | |
"\n", | |
"\n", | |
"In this feature scaling task, we remove the mean from each feature to centre it on zero. Thus, we standardize a dataset \n", | |
"along any axis. Scikit-Learn provides **scale** class for this purpose. \n", | |
"\n", | |
"\n", | |
"The syntax for this purpose is given below:-\n", | |
"\n", | |
"\n", | |
"`from sklearn.preprocessing import scale`\n", | |
"\n", | |
"`scl = scale()`\n", | |
"\n", | |
"`X_train_scl = scl.fit_transform(X_train)`\n", | |
"\n", | |
"`X_test_scl = scl.transform(X_test)`\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 22, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Create sample dataset to demonstrate standardization using scale\n", | |
"\n", | |
"data2 = [[5, -5, 1],\n", | |
" [2, -2, 3],\n", | |
" [1, 0, -1]]\n", | |
"\n", | |
"df7 = pd.DataFrame(data2, columns = ['x4', 'x5', 'x6'])" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 23, | |
"metadata": {}, | |
"outputs": [], | |
"source": [ | |
"# Use scale to apply standardization\n", | |
"\n", | |
"from sklearn.preprocessing import scale\n", | |
"scaled_df7 = scale(df7)" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 24, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([[ 1.37281295, -1.29777137, 0. ],\n", | |
" [-0.39223227, 0.16222142, 1.22474487],\n", | |
" [-0.98058068, 1.13554995, -1.22474487]])" | |
] | |
}, | |
"execution_count": 24, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# Print the results\n", | |
"\n", | |
"scaled_df7" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"**Interpretation**\n", | |
"\n", | |
"The dataset is now standardized along axis = 0. So, the feature variables are now standardized independently." | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 25, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([0, 0, 0])" | |
] | |
}, | |
"execution_count": 25, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# The scaled data has zero mean\n", | |
"\n", | |
"(scaled_df7.mean(axis = 0).astype(int))" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 26, | |
"metadata": {}, | |
"outputs": [ | |
{ | |
"data": { | |
"text/plain": [ | |
"array([1., 1., 1.])" | |
] | |
}, | |
"execution_count": 26, | |
"metadata": {}, | |
"output_type": "execute_result" | |
} | |
], | |
"source": [ | |
"# The scaled data has unit variance\n", | |
"\n", | |
"(scaled_df7.std(axis = 0))" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"**Interpretation**\n", | |
"\n", | |
"We can see that the scaled data has zero mean and unit variance. " | |
] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 3", | |
"language": "python", | |
"name": "python3" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.7.0" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 2 | |
} |
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