{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Dataset \n",
"We will use the \"bikes_rent.csv\" dataset, which records calendar information and weather conditions that characterize automated bike rental locations and the target: number of rentals on that day, named **cnt**. The latter we will predict them in linear regression models and evaluate models quality. Thus, we will solve the regression problem."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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season
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yr
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mnth
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holiday
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weekday
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workingday
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weathersit
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temp
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atemp
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hum
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windspeed(mph)
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windspeed(ms)
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cnt
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"text/plain": [
" season yr mnth holiday weekday workingday weathersit temp \\\n",
"0 1 0 1 0 6 0 2 14.110847 \n",
"1 1 0 1 0 0 0 2 14.902598 \n",
"2 1 0 1 0 1 1 1 8.050924 \n",
"3 1 0 1 0 2 1 1 8.200000 \n",
"4 1 0 1 0 3 1 1 9.305237 \n",
"\n",
" atemp hum windspeed(mph) windspeed(ms) cnt \n",
"0 18.18125 80.5833 10.749882 4.805490 985 \n",
"1 17.68695 69.6087 16.652113 7.443949 801 \n",
"2 9.47025 43.7273 16.636703 7.437060 1349 \n",
"3 10.60610 59.0435 10.739832 4.800998 1562 \n",
"4 11.46350 43.6957 12.522300 5.597810 1600 "
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('bikes_rent.csv')\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For each rental day, the following attributes/dataset features are known (as they were specified in the data source):\n",
" - season: 1-spring, 2-summer, 3-autumn, 4-winter\n",
" - yr: 0 - 2011, 1 - 2012\n",
" - mnth: from 1 to 12\n",
" - holiday: 0 - no holiday, 1 - there is a holiday\n",
" - weekday: 0 to 6\n",
" - workingday: 0 - non-working day, 1 - working day\n",
" - weathersit: weather favorability rating from 1 (clear day) to 4 (rain, fog)\n",
" - temp: temperature in Celsius degrees\n",
" - atemp: temperature feels like, Celsius degrees\n",
" - hum: humidity\n",
" - windspeed(mph): wind speed in miles per hour\n",
" - windspeed (ms): wind speed in meters per second\n",
" - cnt: the number of rented bicycles (this is the target attribute, we will predict it thru a built model)\n",
"\n",
"So, we have real (*temp\tatemp, hum,\twindspeed(mph),\twindspeed(ms)*), binary (*holiday, workingday*), and nominal (ordinal) features (eg. *season: 1-spring, 2-summer, 3-autumn, 4-winter; weekday, weathersit*). All of them might be worked with as real features. \n",
"\n",
"One can work with nominal attributes as with real ones, because they are in an order. Let's look at the figures below, showing how the target attribute (**cnt**) depends on each of the features.\n",
"\n",
"#### Feature target plots"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"
"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=3, ncols=4, figsize=(15, 10))\n",
"for idx, feature in enumerate(df.columns[:-1]):\n",
" df.plot(feature, \"cnt\", subplots=True, kind=\"scatter\", ax=axes[idx // 4, idx % 4])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One can see from the charts that there is the linear dependence of the target of some features, eg. *season, atemp*."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Features and target correlations\n",
"#### Pairwise correlations between all featues except for the target\n",
"Let's more strictly estimate the level of linear dependence between the features and the target variable. A good measure of the linear relationship between two vectors is the [Pearson correlation](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient). In the **Pandas** it can be calculated using two dataframe methods: **corr** and **corrwith**. The **df.corr** method calculates the correlation matrix of all features from the dataframe. The **df.corrwith** method needs to supply another dataframe as an argument, and then it will calculate pairwise correlations between the features from the function argument and the original dataframe."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
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1.000000
\n",
"
0.991702
\n",
"
0.126963
\n",
"
-0.157944
\n",
"
-0.157944
\n",
"
\n",
"
\n",
"
atemp
\n",
"
0.342876
\n",
"
0.046106
\n",
"
0.227459
\n",
"
-0.032507
\n",
"
-0.007537
\n",
"
0.052182
\n",
"
-0.121583
\n",
"
0.991702
\n",
"
1.000000
\n",
"
0.139988
\n",
"
-0.183643
\n",
"
-0.183643
\n",
"
\n",
"
\n",
"
hum
\n",
"
0.205445
\n",
"
-0.110651
\n",
"
0.222204
\n",
"
-0.015937
\n",
"
-0.052232
\n",
"
0.024327
\n",
"
0.591045
\n",
"
0.126963
\n",
"
0.139988
\n",
"
1.000000
\n",
"
-0.248489
\n",
"
-0.248489
\n",
"
\n",
"
\n",
"
windspeed(mph)
\n",
"
-0.229046
\n",
"
-0.011817
\n",
"
-0.207502
\n",
"
0.006292
\n",
"
0.014282
\n",
"
-0.018796
\n",
"
0.039511
\n",
"
-0.157944
\n",
"
-0.183643
\n",
"
-0.248489
\n",
"
1.000000
\n",
"
1.000000
\n",
"
\n",
"
\n",
"
windspeed(ms)
\n",
"
-0.229046
\n",
"
-0.011817
\n",
"
-0.207502
\n",
"
0.006292
\n",
"
0.014282
\n",
"
-0.018796
\n",
"
0.039511
\n",
"
-0.157944
\n",
"
-0.183643
\n",
"
-0.248489
\n",
"
1.000000
\n",
"
1.000000
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" season yr mnth holiday weekday workingday \\\n",
"season 1.000000 -0.001844 0.831440 -0.010537 -0.003080 0.012485 \n",
"yr -0.001844 1.000000 -0.001792 0.007954 -0.005461 -0.002013 \n",
"mnth 0.831440 -0.001792 1.000000 0.019191 0.009509 -0.005901 \n",
"holiday -0.010537 0.007954 0.019191 1.000000 -0.101960 -0.253023 \n",
"weekday -0.003080 -0.005461 0.009509 -0.101960 1.000000 0.035790 \n",
"workingday 0.012485 -0.002013 -0.005901 -0.253023 0.035790 1.000000 \n",
"weathersit 0.019211 -0.048727 0.043528 -0.034627 0.031087 0.061200 \n",
"temp 0.334315 0.047604 0.220205 -0.028556 -0.000170 0.052660 \n",
"atemp 0.342876 0.046106 0.227459 -0.032507 -0.007537 0.052182 \n",
"hum 0.205445 -0.110651 0.222204 -0.015937 -0.052232 0.024327 \n",
"windspeed(mph) -0.229046 -0.011817 -0.207502 0.006292 0.014282 -0.018796 \n",
"windspeed(ms) -0.229046 -0.011817 -0.207502 0.006292 0.014282 -0.018796 \n",
"\n",
" weathersit temp atemp hum windspeed(mph) \\\n",
"season 0.019211 0.334315 0.342876 0.205445 -0.229046 \n",
"yr -0.048727 0.047604 0.046106 -0.110651 -0.011817 \n",
"mnth 0.043528 0.220205 0.227459 0.222204 -0.207502 \n",
"holiday -0.034627 -0.028556 -0.032507 -0.015937 0.006292 \n",
"weekday 0.031087 -0.000170 -0.007537 -0.052232 0.014282 \n",
"workingday 0.061200 0.052660 0.052182 0.024327 -0.018796 \n",
"weathersit 1.000000 -0.120602 -0.121583 0.591045 0.039511 \n",
"temp -0.120602 1.000000 0.991702 0.126963 -0.157944 \n",
"atemp -0.121583 0.991702 1.000000 0.139988 -0.183643 \n",
"hum 0.591045 0.126963 0.139988 1.000000 -0.248489 \n",
"windspeed(mph) 0.039511 -0.157944 -0.183643 -0.248489 1.000000 \n",
"windspeed(ms) 0.039511 -0.157944 -0.183643 -0.248489 1.000000 \n",
"\n",
" windspeed(ms) \n",
"season -0.229046 \n",
"yr -0.011817 \n",
"mnth -0.207502 \n",
"holiday 0.006292 \n",
"weekday 0.014282 \n",
"workingday -0.018796 \n",
"weathersit 0.039511 \n",
"temp -0.157944 \n",
"atemp -0.183643 \n",
"hum -0.248489 \n",
"windspeed(mph) 1.000000 \n",
"windspeed(ms) 1.000000 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# df2 - df without the column 'cnt', the target\n",
"df2 = df[df.columns[:-1]]\n",
"# alternative: df2 = df[df.columns.difference(['cnt'])]\n",
"# get features correlation coefficients\n",
"df2.corr(method='pearson')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Pairwise correlations between featues and the target"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"season 0.406100\n",
"yr 0.566710\n",
"mnth 0.279977\n",
"holiday -0.068348\n",
"weekday 0.067443\n",
"workingday 0.061156\n",
"weathersit -0.297391\n",
"temp 0.627494\n",
"atemp 0.631066\n",
"hum -0.100659\n",
"windspeed(mph) -0.234545\n",
"windspeed(ms) -0.234545\n",
"cnt 1.000000\n",
"dtype: float64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# kitchen sink solution: pd.concat([df,df],axis=1).corr()\n",
"df.corrwith(df['cnt'], axis=0, method='pearson')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The sample above shows features correlations with the target. All of them are not *null*. That means that we can solve the problem using linear methods."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The [graphs above](#Feature-target-plots) show that some of the features are similar to each other. So let's also calculate the correlations between the real features: *temp, atemp, hum, windspeed(mph), windspeed(ms) and cnt*"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
temp
\n",
"
atemp
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"
hum
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windspeed(mph)
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"
windspeed(ms)
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"
cnt
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"
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" \n",
" \n",
"
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temp
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1.000000
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0.991702
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0.126963
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0.627494
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atemp
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0.991702
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1.000000
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0.139988
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-0.183643
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-0.183643
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0.631066
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"
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hum
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0.126963
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0.139988
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1.000000
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-0.248489
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"
-0.248489
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"
-0.100659
\n",
"
\n",
"
\n",
"
windspeed(mph)
\n",
"
-0.157944
\n",
"
-0.183643
\n",
"
-0.248489
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"
1.000000
\n",
"
1.000000
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-0.234545
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"
\n",
"
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"
windspeed(ms)
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"
-0.157944
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"
-0.183643
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-0.248489
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"
1.000000
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"
1.000000
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"
-0.234545
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"
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"
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"
cnt
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"
0.627494
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"
0.631066
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-0.100659
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-0.234545
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-0.234545
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"
1.000000
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" temp atemp hum windspeed(mph) windspeed(ms) \\\n",
"temp 1.000000 0.991702 0.126963 -0.157944 -0.157944 \n",
"atemp 0.991702 1.000000 0.139988 -0.183643 -0.183643 \n",
"hum 0.126963 0.139988 1.000000 -0.248489 -0.248489 \n",
"windspeed(mph) -0.157944 -0.183643 -0.248489 1.000000 1.000000 \n",
"windspeed(ms) -0.157944 -0.183643 -0.248489 1.000000 1.000000 \n",
"cnt 0.627494 0.631066 -0.100659 -0.234545 -0.234545 \n",
"\n",
" cnt \n",
"temp 0.627494 \n",
"atemp 0.631066 \n",
"hum -0.100659 \n",
"windspeed(mph) -0.234545 \n",
"windspeed(ms) -0.234545 \n",
"cnt 1.000000 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df3 = df[['temp', 'atemp', 'hum', 'windspeed(mph)', 'windspeed(ms)', 'cnt']]\n",
"df3.corr(method='pearson')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As expected, there are ones **`1`** on the diagonals. However, there are two more pairs of strongly correlated columns in the matrix: *temp* and *atemp* (correlated by nature) and two *windspeed* features (because one is just a translation of some units into others). Later we will see that this effects negatively on the training of the linear model."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's take a look at the mean values of the features to estimate the scale of the features and the fraction of `1` for binary features."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"season 2.496580\n",
"yr 0.500684\n",
"mnth 6.519836\n",
"holiday 0.028728\n",
"weekday 2.997264\n",
"workingday 0.683995\n",
"weathersit 1.395349\n",
"temp 20.310776\n",
"atemp 23.717699\n",
"hum 62.789406\n",
"windspeed(mph) 12.762576\n",
"windspeed(ms) 5.705220\n",
"cnt 4504.348837\n",
"dtype: float64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The features are of different scale. So for our further work, we better normalize the feature objects matrix."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem one: [Collinear](https://en.wikipedia.org/wiki/Multicollinearity) (highly correlated) features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, in our data, some attributes duplicates another ones and there are two more very similar ones. Of course, we could remove the duplicates right away, but let's see how model would have been trained if we hadn't noticed this problem.\n",
"\n",
"To begin with, we will scale or standardize the features. From each feature, we will subtract its average (mean) and divide it by the standard deviation (std). This can be done using the **scale()** method.\n",
"\n",
"In addition we shuffle the dataset; this is required for cross-validation. "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import scale\n",
"from sklearn.utils import shuffle"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" season yr mnth holiday weekday workingday weathersit temp \\\n",
"488 2 1 5 0 4 1 2 22.960000 \n",
"421 1 1 2 0 0 0 1 11.445847 \n",
"91 2 0 4 0 6 0 2 12.915000 \n",
"300 4 0 10 0 5 1 2 13.564153 \n",
"177 3 0 6 0 1 1 2 27.982500 \n",
"\n",
" atemp hum windspeed(mph) windspeed(ms) cnt \n",
"488 26.86210 76.8333 8.957632 4.004306 6421 \n",
"421 13.41540 41.0000 13.750343 6.146778 3389 \n",
"91 15.78185 65.3750 13.208782 5.904686 2252 \n",
"300 15.94060 58.5833 15.375093 6.873086 3747 \n",
"177 31.85020 65.8333 7.208396 3.222350 4708 \n"
]
}
],
"source": [
"df_shuffled = shuffle(df, random_state=123)\n",
"print(df_shuffled.head())\n",
"X = scale(df_shuffled[df_shuffled.columns[:-1]])\n",
"y = df_shuffled[\"cnt\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Train linear model\n",
"Let's train a linear regression model on our dataset and look at the feature weights. The acquired weights are in the *coef_* parameter of a regressor class."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Linear model coefficients:\n",
" [ 5.70865954e+02 1.02196587e+03 -1.41301029e+02 -8.67565864e+01\n",
" 1.37223034e+02 5.63936928e+01 -3.30228163e+02 3.67479222e+02\n",
" 5.85554695e+02 -1.45612324e+02 1.24575770e+13 -1.24575770e+13]\n",
"\n",
"Coefficients and their weights:\n"
]
},
{
"data": {
"text/plain": [
"[('season', 570.87),\n",
" ('yr', 1021.97),\n",
" ('mnth', -141.3),\n",
" ('holiday', -86.76),\n",
" ('weekday', 137.22),\n",
" ('workingday', 56.39),\n",
" ('weathersit', -330.23),\n",
" ('temp', 367.48),\n",
" ('atemp', 585.55),\n",
" ('hum', -145.61),\n",
" ('windspeed(mph)', 12457576985764.57),\n",
" ('windspeed(ms)', -12457576985963.02)]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import linear_model\n",
" # build a model\n",
"linear_regressor = linear_model.LinearRegression() \n",
" # train the model using all data\n",
"linear_regressor.fit(X, y) \n",
" # output coefficients\n",
"print(\"Linear model coefficients:\\n\", linear_regressor.coef_ )\n",
" # get the model predictions \n",
" # predictions = linear_regressor.predict() \n",
"print('\\nCoefficients and their weights:')\n",
"[(pair[0], round(pair[1],2)) for pair in zip(df.columns, linear_regressor.coef_)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We see that the weights for linearly dependent features are significantly greater in absolute values than for other features."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To better understand why this happened, let's recall the [analytical formula used to calculate the weights of a linear model in the least squares method](/linear-regression-stochastic-gradient-descent/#theory1):\n",
"\n",
"$$w = (X^TX)^{-1} X^T y$$\n",
"\n",
"If there are collinear (linearly dependent) columns in X, the matrix $𝑋^𝑇𝑋$ becomes degenerate one. The formula then ceases to be correct. The more dependent the features are, the smaller the determinant of this matrix is and the worse the approximation of 𝑋\\*𝑤≈𝑦 is. This situation is called the **multicollinearity problem**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The *temp-atemp* pair had slightly less correlated variables/weights. But in practice, one should always carefully monitor the coefficients for similar attributes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The **multicollinearity problem** solution is to regularize the linear model. The norm of weights multiplied by the regularization coefficient $\\alpha$ (L1 or L2) is added to the optimized functional $X*w$. In the first case (**L1**), the method is called Lasso, and in the second (**L2**), Ridge. More about adding regularization into Linear Regression model one can see [here](/add-regularization-in-linear-regression-model/)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We train the [Ridge](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html) и [Lasso](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html?highlight=lasso#sklearn.linear_model.Lasso) regressors with the default parameters and make sure that the problem with the weights is resolved."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import Lasso, Ridge"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Lasso regularization coefficients/weights:\n",
" [ 5.60241616e+02 1.01946349e+03 -1.28730627e+02 -8.61527813e+01\n",
" 1.37347894e+02 5.52123706e+01 -3.32369857e+02 3.76363236e+02\n",
" 5.76530794e+02 -1.44129155e+02 -1.97139689e+02 -2.80504168e-08]\n",
"\n",
"Intercept: 4504.3488372093025\n",
"\n",
"Rounded coef: [560.242, 1019.463, -128.731, -86.153, 137.348, 55.212, -332.37, 376.363, 576.531, -144.129, -197.14, -0.0]\n"
]
}
],
"source": [
"lasso = Lasso()\n",
"lasso.fit(X, y)\n",
"print('Lasso regularization coefficients/weights:\\n', lasso.coef_)\n",
"print('\\nIntercept:', lasso.intercept_)\n",
"print( '\\nRounded coef:', list(map(lambda c: round(c, 3) , lasso.coef_) )) "
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ridge regularization coefficients/weights:\n",
" [ 563.06457225 1018.94837879 -131.87332028 -86.746098 138.00511118\n",
" 55.90311038 -332.3497885 386.45788919 566.34704706 -145.0713273\n",
" -99.25944108 -99.25944115]\n",
"\n",
"Intercept: 4504.3488372093025\n",
"\n",
"Rounded coef: [563.065, 1018.948, -131.873, -86.746, 138.005, 55.903, -332.35, 386.458, 566.347, -145.071, -99.259, -99.259]\n"
]
}
],
"source": [
"ridge = Ridge()\n",
"ridge.fit(X, y)\n",
"print('Ridge regularization coefficients/weights:\\n', ridge.coef_)\n",
"print('\\nIntercept:', ridge.intercept_)\n",
"print('\\nRounded coef:', list(map(lambda c: round(c, 3) , ridge.coef_) )) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem two: Uninformative features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In contrast to L2 regularization, L1 [resets the weights for some features](/linear-models-sklearn-linear_model-regression/#Lasso-regularizator-or-L1-for-the-Linear-Regression).\n",
"\n",
"#### Model coefficients as function of alpha\n",
"Let's see how the weights change with an increase in the **regularization coefficient alpha**.\n",
"\n",
"**coefs_lasso** is the matrix of weights of size (number of regressors) x (number of features)."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"alphas = np.arange(1, 500, 50)\n",
"coefs_lasso = np.zeros((alphas.shape[0], X.shape[1])) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For each coefficient value from **alphas** set we train the Lasso regressor and write the weights into the corresponding rows of the **coefs_lasso** matrix. Then we train the Ridge regressor and write the weights into **coefs_ridge** matrix."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Matrix `coefs_lasso` size: (10, 12) \n",
"Alpha values: [ 1 51 101 151 201 251 301 351 401 451]\n",
"Lasso coefficients, head:\n",
" [[ 5.60241616e+02 1.01946349e+03 -1.28730627e+02 -8.61527813e+01\n",
" 1.37347894e+02 5.52123706e+01 -3.32369857e+02 3.76363236e+02\n",
" 5.76530794e+02 -1.44129155e+02 -1.97139689e+02 -2.80504168e-08]\n",
" [ 4.10969632e+02 9.77019409e+02 -0.00000000e+00 -5.34489688e+01\n",
" 9.19434374e+01 1.75372118e+01 -3.18125568e+02 3.22829934e+02\n",
" 6.10031512e+02 -9.10689615e+01 -1.45066095e+02 -2.29877063e-08]\n",
" [ 3.70077089e+02 9.35945490e+02 0.00000000e+00 -1.21619360e+01\n",
" 4.88886342e+01 0.00000000e+00 -3.08805664e+02 2.69417263e+02\n",
" 6.32502623e+02 -2.75042876e+01 -9.37749037e+01 -2.41652170e-08]\n",
" [ 3.32835717e+02 8.91870058e+02 0.00000000e+00 -0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00 -2.79616688e+02 2.11052030e+02\n",
" 6.62920880e+02 -0.00000000e+00 -5.01551472e+01 -2.62761604e-08]\n",
" [ 2.98134448e+02 8.45652857e+02 0.00000000e+00 -0.00000000e+00\n",
" 0.00000000e+00 0.00000000e+00 -2.35571345e+02 1.24144807e+02\n",
" 7.25379483e+02 -0.00000000e+00 -1.26461769e+01 -2.78781252e-08]]\n",
"\n",
"Rounded Lasso coefficients:\n",
" [[560.242, 1019.463, -128.731, -86.153, 137.348, 55.212, -332.37, 376.363, 576.531, -144.129, -197.14, -0.0], [410.97, 977.019, -0.0, -53.449, 91.943, 17.537, -318.126, 322.83, 610.032, -91.069, -145.066, -0.0], [370.077, 935.945, 0.0, -12.162, 48.889, 0.0, -308.806, 269.417, 632.503, -27.504, -93.775, -0.0], [332.836, 891.87, 0.0, -0.0, 0.0, 0.0, -279.617, 211.052, 662.921, -0.0, -50.155, -0.0], [298.134, 845.653, 0.0, -0.0, 0.0, 0.0, -235.571, 124.145, 725.379, -0.0, -12.646, -0.0], [258.927, 799.237, 0.0, -0.0, 0.0, 0.0, -190.822, 72.076, 750.363, -0.0, -0.0, -0.0], [217.428, 752.721, 0.0, -0.0, 0.0, 0.0, -145.713, 37.715, 756.297, -0.0, -0.0, -0.0], [175.93, 706.204, 0.0, -0.0, 0.0, 0.0, -100.605, 3.661, 761.926, -0.0, -0.0, -0.0], [134.628, 659.633, 0.0, -0.0, 0.0, 0.0, -55.511, 0.0, 737.348, -0.0, -0.0, -0.0], [93.352, 613.055, 0.0, -0.0, 0.0, 0.0, -10.419, 0.0, 709.131, -0.0, -0.0, -0.0]]\n"
]
}
],
"source": [
"print('Matrix `coefs_lasso` size:', (alphas.shape[0], X.shape[1]), \"\\nAlpha values:\", alphas )\n",
"for i, al in enumerate(alphas):\n",
" lasso = Lasso(alpha = al)\n",
" lasso.fit(X, y)\n",
" for j, coef in enumerate(lasso.coef_):\n",
" coefs_lasso[i][j]=coef\n",
" #print(list(map(lambda c: round(c, 3) , coefs_lasso[i]) ))\n",
"# list(map(lambda c: round(c, 3) , coefs_lasso) ))\n",
"print('Lasso coefficients, head:\\n', coefs_lasso[:5])\n",
"print('\\nRounded Lasso coefficients:\\n', list(map(lambda i: list(map(lambda j: round(j, 3), i)), coefs_lasso)))\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[563.065, 1018.948, -131.873, -86.746, 138.005, 55.903, -332.35, 386.458, 566.347, -145.071, -99.259, -99.259]\n",
"[461.179, 954.308, -41.565, -84.913, 126.604, 54.252, -313.275, 458.901, 481.444, -151.291, -101.627, -101.627]\n",
"[403.977, 898.084, 5.674, -81.911, 117.941, 52.728, -298.409, 455.29, 467.431, -152.686, -102.102, -102.102]\n",
"[366.604, 848.463, 34.027, -78.772, 110.68, 51.257, -286.125, 447.48, 455.754, -151.483, -102.005, -102.005]\n",
"[339.745, 804.251, 52.49, -75.717, 104.403, 49.842, -275.486, 438.51, 444.764, -148.944, -101.586, -101.586]\n",
"[319.159, 764.561, 65.152, -72.82, 98.879, 48.485, -266.003, 429.214, 434.235, -145.698, -100.965, -100.965]\n",
"[302.636, 728.709, 74.138, -70.099, 93.956, 47.185, -257.391, 419.926, 424.122, -142.089, -100.209, -100.209]\n",
"[288.913, 696.146, 80.66, -67.555, 89.529, 45.943, -249.471, 410.8, 414.409, -138.317, -99.361, -99.361]\n",
"[277.213, 666.43, 85.459, -65.179, 85.519, 44.757, -242.124, 401.912, 405.086, -134.499, -98.449, -98.449]\n",
"[267.031, 639.195, 89.012, -62.959, 81.865, 43.625, -235.264, 393.298, 396.138, -130.71, -97.493, -97.493]\n"
]
}
],
"source": [
"coefs_ridge = np.zeros((alphas.shape[0], X.shape[1]))\n",
"for i, al in enumerate(alphas):\n",
" ridge = Ridge(alpha = al)\n",
" ridge.fit(X, y)\n",
" for j, coef in enumerate(ridge.coef_):\n",
" coefs_ridge[i][j]=coef\n",
" print(list(map(lambda c: round(c, 3) , coefs_ridge[i]) ))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### The weights dependence on alpha dynamics\n",
"We visualize the dynamics of the weights as the regularization parameter $\\alpha$ increases."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Ridge')"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
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