diff --git a/your-code/main.ipynb b/your-code/main.ipynb index 8818016..b85d556 100644 --- a/your-code/main.ipynb +++ b/your-code/main.ipynb @@ -12,14 +12,14 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 183, "metadata": {}, "outputs": [], "source": [ "# Libraries\n", "import pandas as pd\n", "import numpy as np\n", - "from sklearn import datasets\n" + "from sklearn.datasets import load_diabetes\n" ] }, { @@ -40,11 +40,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 184, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "# your code here\n", + "\n", + "diabetes_data = load_diabetes()\n", + "\n" ] }, { @@ -56,22 +59,23 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 185, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename'])" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Keys of diabetes dataset:\n", + "dict_keys(['data', 'target', 'frame', 'DESCR', 'feature_names', 'data_filename', 'target_filename'])\n" + ] } ], "source": [ - "# your code here\n" + "# your code here\n", + "print(\"\\nKeys of diabetes dataset:\")\n", + "print(diabetes_data.keys())" ] }, { @@ -87,10 +91,8 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "scrolled": false - }, + "execution_count": 186, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -138,7 +140,7 @@ } ], "source": [ - "# your code here\n" + "print(diabetes_data.DESCR)" ] }, { @@ -156,11 +158,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 187, "metadata": {}, "outputs": [], "source": [ - "# your answer here \n" + "# your answer here \n", + "# 1. Attributes: 10 (age, sex, bmi, bp, s1-s6)\n", + "# 2. Data \"Target\" = Column 11 is a quantitative measure of disease progression one year after baseline\n", + "# 3. 442 Records" ] }, { @@ -174,55 +179,41 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 188, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Shape of 'data' :\n" + "Shape of Data: \n", + "(442, 10)\n" ] - }, - { - "data": { - "text/plain": [ - "(442, 10)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "# your code here\n" + "print(\"Shape of Data: \")\n", + "print(diabetes_data.data.shape)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 189, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Shape of 'target' :\n" + "Shape of Target: \n", + "(442,)\n" ] - }, - { - "data": { - "text/plain": [ - "(442,)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" } ], - "source": [] + "source": [ + "print(\"Shape of Target: \")\n", + "print(diabetes_data.target.shape)" + ] }, { "cell_type": "markdown", @@ -258,11 +249,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 190, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "# your code here\n", + "from sklearn.linear_model import LinearRegression" ] }, { @@ -274,11 +266,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 191, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "# your code here\n", + "diabetes_model = LinearRegression()" ] }, { @@ -292,11 +285,20 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 192, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "# Getting X and y:\n", + "diabetes_X, diabetes_y = datasets.load_diabetes(return_X_y=True)\n", + "\n", + "# Split the data into training/testing sets with 20 last records for test data\n", + "diabetes_X_train = diabetes_X[:-20]\n", + "diabetes_X_test = diabetes_X[-20:]\n", + "\n", + "\n", + "diabetes_y_train = diabetes_y[:-20]\n", + "diabetes_y_test = diabetes_y[-20:]" ] }, { @@ -308,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 193, "metadata": {}, "outputs": [ { @@ -317,46 +319,55 @@ "LinearRegression()" ] }, - "execution_count": 14, + "execution_count": 193, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# your code here\n" + "# Fitting the Model on diabetes X and y\n", + "diabetes_model.fit(diabetes_X_train, diabetes_y_train)" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 194, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Intercept: 152.76430691633442\n" + "Intercept: \n", + " 152.76430691633442\n" ] } ], - "source": [] + "source": [ + "# The Intercept\n", + "print('Intercept: \\n', diabetes_model.intercept_)" + ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 195, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Coefficients: [ 3.03499549e-01 -2.37639315e+02 5.10530605e+02 3.27736980e+02\n", + "Coefficients: \n", + " [ 3.03499549e-01 -2.37639315e+02 5.10530605e+02 3.27736980e+02\n", " -8.14131709e+02 4.92814588e+02 1.02848452e+02 1.84606489e+02\n", " 7.43519617e+02 7.60951722e+01]\n" ] } ], - "source": [] + "source": [ + "# The coefficients\n", + "print('Coefficients: \\n', diabetes_model.coef_)" + ] }, { "cell_type": "markdown", @@ -376,11 +387,29 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 196, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "array([197.61846908, 155.43979328, 172.88665147, 111.53537279,\n", + " 164.80054784, 131.06954875, 259.12237761, 100.47935157,\n", + " 117.0601052 , 124.30503555, 218.36632793, 61.19831284,\n", + " 132.25046751, 120.3332925 , 52.54458691, 194.03798088,\n", + " 102.57139702, 123.56604987, 211.0346317 , 52.60335674])" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# your code here\n" + "# your code here\n", + "# Make predictions using the testing set\n", + "diabetes_y_pred = diabetes_model.predict(diabetes_X_test)\n", + "diabetes_y_pred" ] }, { @@ -392,60 +421,53 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 197, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "diabetes_target_test:\n" + "diabetes_y_test: \n", + "\n", + "\n", + "[233. 91. 111. 152. 120. 67. 310. 94. 183. 66. 173. 72. 49. 64.\n", + " 48. 178. 104. 132. 220. 57.]\n" ] - }, - { - "data": { - "text/plain": [ - "array([233., 91., 111., 152., 120., 67., 310., 94., 183., 66., 173.,\n", - " 72., 49., 64., 48., 178., 104., 132., 220., 57.])" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" } ], "source": [ - "# your code here\n" + "# Printing diabetes_y_test:\n", + "print(\"diabetes_y_test: \")\n", + "print(\"\\n\")\n", + "print(diabetes_y_test)" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 198, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "test prediction:\n" + "test prediction: \n", + "\n", + "\n", + "[197.61846908 155.43979328 172.88665147 111.53537279 164.80054784\n", + " 131.06954875 259.12237761 100.47935157 117.0601052 124.30503555\n", + " 218.36632793 61.19831284 132.25046751 120.3332925 52.54458691\n", + " 194.03798088 102.57139702 123.56604987 211.0346317 52.60335674]\n" ] - }, - { - "data": { - "text/plain": [ - "array([197.61846908, 155.43979328, 172.88665147, 111.53537279,\n", - " 164.80054784, 131.06954875, 259.12237761, 100.47935157,\n", - " 117.0601052 , 124.30503555, 218.36632793, 61.19831284,\n", - " 132.25046751, 120.3332925 , 52.54458691, 194.03798088,\n", - " 102.57139702, 123.56604987, 211.0346317 , 52.60335674])" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" } ], - "source": [] + "source": [ + "# Printing diabetes_y_pred:\n", + "print(\"test prediction: \")\n", + "print(\"\\n\")\n", + "print(diabetes_y_pred)" + ] }, { "cell_type": "markdown", @@ -456,11 +478,12 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 199, "metadata": {}, "outputs": [], "source": [ - "# your answer here \n" + "# your answer here \n", + "# No its now the same!" ] }, { @@ -495,50 +518,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 200, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " OLS Regression Results \n", - "==============================================================================\n", - "Dep. Variable: y R-squared: 0.512\n", - "Model: OLS Adj. R-squared: 0.500\n", - "Method: Least Squares F-statistic: 43.16\n", - "Date: Mon, 10 May 2021 Prob (F-statistic): 4.64e-58\n", - "Time: 17:52:26 Log-Likelihood: -2281.1\n", - "No. Observations: 422 AIC: 4584.\n", - "Df Residuals: 411 BIC: 4629.\n", - "Df Model: 10 \n", - "Covariance Type: nonrobust \n", - "==============================================================================\n", - " coef std err t P>|t| [0.025 0.975]\n", - "------------------------------------------------------------------------------\n", - "x1 0.3035 61.286 0.005 0.996 -120.169 120.776\n", - "x2 -237.6393 62.837 -3.782 0.000 -361.162 -114.117\n", - "x3 510.5306 68.156 7.491 0.000 376.553 644.508\n", - "x4 327.7370 66.876 4.901 0.000 196.275 459.199\n", - "x5 -814.1317 424.044 -1.920 0.056 -1647.697 19.434\n", - "x6 492.8146 344.227 1.432 0.153 -183.850 1169.480\n", - "x7 102.8485 219.463 0.469 0.640 -328.561 534.258\n", - "x8 184.6065 167.336 1.103 0.271 -144.334 513.547\n", - "x9 743.5196 175.359 4.240 0.000 398.807 1088.232\n", - "x10 76.0952 68.293 1.114 0.266 -58.152 210.343\n", - "const 152.7643 2.658 57.469 0.000 147.539 157.990\n", - "==============================================================================\n", - "Omnibus: 1.544 Durbin-Watson: 2.026\n", - "Prob(Omnibus): 0.462 Jarque-Bera (JB): 1.421\n", - "Skew: 0.004 Prob(JB): 0.491\n", - "Kurtosis: 2.716 Cond. No. 224.\n", - "==============================================================================\n", - "\n", - "Warnings:\n", - "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n" - ] - } - ], + "outputs": [], "source": [ "# your code here\n", "import statsmodels.api as sm\n", @@ -562,7 +544,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 201, "metadata": {}, "outputs": [], "source": [ @@ -587,11 +569,12 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 202, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" + "# your code here\n", + "auto = pd.read_csv(\"/Users/timeisenmenger/Desktop/Ironhack /Week7/Week7-Lab3/lab-supervised-learning-sklearn/data/auto-mpg.csv\")" ] }, { @@ -603,7 +586,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 203, "metadata": {}, "outputs": [ { @@ -713,13 +696,14 @@ "4 70 \\t\"ford torino\" " ] }, - "execution_count": 27, + "execution_count": 203, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# your code here\n" + "# your code here\n", + "auto.head()" ] }, { @@ -731,7 +715,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 204, "metadata": {}, "outputs": [ { @@ -757,7 +741,8 @@ } ], "source": [ - "# your code here\n" + "# Getting an overview of the data frame\n", + "auto.info()" ] }, { @@ -769,7 +754,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 205, "metadata": {}, "outputs": [ { @@ -778,19 +763,20 @@ "70" ] }, - "execution_count": 24, + "execution_count": 205, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# your code here\n", - "# OLDEST MODEL\n" + "# OLDEST MODEL\n", + "auto[\"model_year\"].min()" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 206, "metadata": {}, "outputs": [ { @@ -799,13 +785,14 @@ "82" ] }, - "execution_count": 25, + "execution_count": 206, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# NEWEST MODEL \n" + "# NEWEST MODEL \n", + "auto[\"model_year\"].max()" ] }, { @@ -817,236 +804,883 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Find the frequency table for the `cylinders` column using the `value_counts()` function. How many possible values of cylinders are there?" - ] - }, - { - "cell_type": "code", - "execution_count": 29, + "execution_count": 207, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "4 199\n", - "8 103\n", - "6 83\n", - "3 4\n", - "5 3\n", - "Name: cylinders, dtype: int64" + "mpg 0\n", + "cylinders 0\n", + "displacement 0\n", + "horse_power 6\n", + "weight 0\n", + "acceleration 0\n", + "model_year 0\n", + "car_name 0\n", + "dtype: int64" ] }, - "execution_count": 29, + "execution_count": 207, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# your code here \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We would like to generate a linear regression model that will predict mpg. To do this, first drop the `car_name` column since it does not contain any quantitative data. Next separate the dataframe to predictor and response variables. Separate those into test and training data with 80% of the data in the training set and the remainder in the test set. \n", - "\n", - "Assign the predictor and response training data to `X_train` and `y_train` respectively. Similarly, assign the predictor and response test data to `X_test` and `y_test`.\n", - "\n", - "*Hint: To separate data for training and test, use the `train_test_split` method we used in previous labs.*" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we will processed and peform linear regression on this data to predict the mpg for each vehicle. \n", - "\n", - "#### In the next cell, create an instance of the linear regression model and call it `auto_model`. Fit `auto_model` with your training data." + "# Checking for missing values\n", + "auto.isna().sum()" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 208, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Challenge 4 - Evaluate the Model\n", - "\n", - "In addition to evaluating your model with F-test and t-test, you can also use the *Coefficient of Determination* (a.k.a. *r squared score*). This method does not simply tell *yes* or *no* about the model fit but instead indicates how much variation can be explained by the model. Based on the r squared score, you can decide whether to improve your model in order to obtain a better fit.\n", - "\n", - "You can learn about the r squared score [here](https://en.wikipedia.org/wiki/Coefficient_of_determination). Its formula is:\n", - "\n", - "
\n", - "\n", - "where:\n", - "\n", - "* yi is an actual data point.\n", - "* ŷi is the corresponding data point on the estimated regression line.\n", - "\n", - "By adding the squares of the difference between all yi-ŷi pairs, we have a measure called SSE (*error sum of squares*) which is an application of the r squared score to indicate the extent to which the estimated regression model is different from the actual data. And we attribute that difference to the random error that is unavoidable in the real world. Obviously, we want the SSE value to be as small as possible.\n", - "\n", - "#### In the next cell, compute the predicted *y* based on `X_train` and call it `y_pred`. Then calcualte the r squared score between `y_pred` and `y_train` which indicates how well the estimated regression model fits the training data.\n", - "\n", - "*Hint: r squared score can be calculated using `sklearn.metrics.r2_score` ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html)).*" + "# Dropping rows with missing values\n", + "auto.dropna(axis = 0, how ='any', inplace=True)" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 209, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.8198690008457218" + "mpg 0\n", + "cylinders 0\n", + "displacement 0\n", + "horse_power 0\n", + "weight 0\n", + "acceleration 0\n", + "model_year 0\n", + "car_name 0\n", + "dtype: int64" ] }, - "execution_count": 38, + "execution_count": 209, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# your code here\n" + "# Check again if it worked\n", + "auto.isna().sum()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Our next step is to evaluate the model using the test data. \n", - "\n", - "We would like to ensure that our model is not overfitting the data. This means that our model was made to fit too closely to the training data by being overly complex. If a model is overfitted, it is not generalizable to data outside the training data. In that case, we need to reduce the complexity of the model by removing certain features (variables).\n", - "\n", - "In the cell below, use the model to generate the predicted values for the test data and assign them to `y_test_pred`. Compute the r squared score of the predicted `y_test_pred` and the oberserved `y_test` data." + "Find the frequency table for the `cylinders` column using the `value_counts()` function. How many possible values of cylinders are there?" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 210, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.7507754274816084" + "4 199\n", + "8 103\n", + "6 83\n", + "3 4\n", + "5 3\n", + "Name: cylinders, dtype: int64" ] }, - "execution_count": 39, + "execution_count": 210, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Explaining the results\n", - "\n", - "Please, compare the results for the training and test sets and comment. What can you say?" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [], - "source": [ - "# your answer here\n" + "# Frequency of possible values in cylinders column\n", + "auto[\"cylinders\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# Challenge 5 - Improve the Model Fit\n", + "We would like to generate a linear regression model that will predict mpg. To do this, first drop the `car_name` column since it does not contain any quantitative data. Next separate the dataframe to predictor and response variables. Separate those into test and training data with 80% of the data in the training set and the remainder in the test set. \n", "\n", - "While the most common way to improve the fit of a model is by using [regularization](https://datanice.github.io/machine-learning-101-what-is-regularization-interactive.html), there are other simpler ways to improve model fit. The first is to create a simpler model. The second is to increase the train sample size.\n", + "Assign the predictor and response training data to `X_train` and `y_train` respectively. Similarly, assign the predictor and response test data to `X_test` and `y_test`.\n", "\n", - "Let us start with the easier option and increase our train sample size to 90% of the data. Create a new test train split and name the new predictors and response variables `X_train09`, `X_test09`, `y_train09`, `y_test09`." - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Initialize a new linear regression model. Name this model `auto_model09`. Fit the model to the new sample (training) data." + "*Hint: To separate data for training and test, use the `train_test_split` method we used in previous labs.*" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 211, "metadata": {}, "outputs": [], "source": [ - "# your code here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute the predicted values and r squared score for our new model and new sample data." + "# Dropping \"Car_name\" column\n", + "auto.drop([\"car_name\"], axis='columns', inplace=True)\n" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 212, "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "0.8109551916128583" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "text/html": [ + "
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" + ], + "text/plain": [ + " mpg cylinders displacement horse_power weight acceleration \\\n", + "0 18.0 8 307.0 130.0 3504 12.0 \n", + "1 15.0 8 350.0 165.0 3693 11.5 \n", + "2 18.0 8 318.0 150.0 3436 11.0 \n", + "3 16.0 8 304.0 150.0 3433 12.0 \n", + "4 17.0 8 302.0 140.0 3449 10.5 \n", + "\n", + " model_year \n", + "0 70 \n", + "1 70 \n", + "2 70 \n", + "3 70 \n", + "4 70 " + ] + }, + "execution_count": 212, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check Data Frame again\n", + "auto.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 213, + "metadata": {}, + "outputs": [], + "source": [ + "# Naming my X and y\n", + "# y = mpg (first column, index 0)\n", + "# X = all other columns, starting from index 1\n", + "\n", + "X = auto.iloc[:, 1:].values\n", + "y = auto.iloc[:, :1].values" + ] + }, + { + "cell_type": "code", + "execution_count": 214, + "metadata": {}, + "outputs": [], + "source": [ + "# importing train_test_split from sklearn\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Setting up parameters with test size 20% and training size 80% \n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)" + ] + }, + { + "cell_type": "code", + "execution_count": 215, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[8.000e+00, 4.540e+02, 2.200e+02, 4.354e+03, 9.000e+00, 7.000e+01],\n", + " [4.000e+00, 8.600e+01, 6.500e+01, 1.975e+03, 1.520e+01, 7.900e+01],\n", + " [4.000e+00, 1.210e+02, 1.100e+02, 2.660e+03, 1.400e+01, 7.300e+01],\n", + " ...,\n", + " [4.000e+00, 8.500e+01, 6.500e+01, 2.110e+03, 1.920e+01, 8.000e+01],\n", + " [4.000e+00, 1.560e+02, 9.200e+01, 2.585e+03, 1.450e+01, 8.200e+01],\n", + " [4.000e+00, 1.120e+02, 8.500e+01, 2.575e+03, 1.620e+01, 8.200e+01]])" + ] + }, + "execution_count": 215, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will processed and peform linear regression on this data to predict the mpg for each vehicle. \n", + "\n", + "#### In the next cell, create an instance of the linear regression model and call it `auto_model`. Fit `auto_model` with your training data." + ] + }, + { + "cell_type": "code", + "execution_count": 216, + "metadata": {}, + "outputs": [], + "source": [ + "# Creating instance of LR Model\n", + "\n", + "auto_model = LinearRegression()" + ] + }, + { + "cell_type": "code", + "execution_count": 217, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 217, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Fit the model\n", + "\n", + "auto_model.fit(X_train, y_train)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Challenge 4 - Evaluate the Model\n", + "\n", + "In addition to evaluating your model with F-test and t-test, you can also use the *Coefficient of Determination* (a.k.a. *r squared score*). This method does not simply tell *yes* or *no* about the model fit but instead indicates how much variation can be explained by the model. Based on the r squared score, you can decide whether to improve your model in order to obtain a better fit.\n", + "\n", + "You can learn about the r squared score [here](https://en.wikipedia.org/wiki/Coefficient_of_determination). Its formula is:\n", + "\n", + "
\n", + "\n", + "where:\n", + "\n", + "* yi is an actual data point.\n", + "* ŷi is the corresponding data point on the estimated regression line.\n", + "\n", + "By adding the squares of the difference between all yi-ŷi pairs, we have a measure called SSE (*error sum of squares*) which is an application of the r squared score to indicate the extent to which the estimated regression model is different from the actual data. And we attribute that difference to the random error that is unavoidable in the real world. Obviously, we want the SSE value to be as small as possible.\n", + "\n", + "#### In the next cell, compute the predicted *y* based on `X_train` and call it `y_pred`. Then calcualte the r squared score between `y_pred` and `y_train` which indicates how well the estimated regression model fits the training data.\n", + "\n", + "*Hint: r squared score can be calculated using `sklearn.metrics.r2_score` ([documentation](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html)).*" + ] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[10.84471862],\n", + " [32.0954254 ],\n", + " [23.47903362],\n", + " [27.54206641],\n", + " [26.58301934],\n", + " [32.41072242],\n", + " [29.0820807 ],\n", + " [22.29098186],\n", + " [11.13224522],\n", + " [24.48657958],\n", + " [33.23896902],\n", + " [31.02424542],\n", + " [14.72272886],\n", + " [16.58014186],\n", + " [18.31625476],\n", + " [30.33778579],\n", + " [ 9.86813601],\n", + " [18.92042742],\n", + " [20.5702619 ],\n", + " [22.43241992],\n", + " [20.09654856],\n", + " [13.06576698],\n", + " [18.07699573],\n", 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[24.98124159],\n", + " [22.40776114],\n", + " [23.86570717],\n", + " [16.17895978],\n", + " [13.68872719],\n", + " [28.95159062],\n", + " [12.85567041],\n", + " [31.18154838],\n", + " [32.87566449],\n", + " [29.94611618],\n", + " [11.34686965],\n", + " [17.9381393 ],\n", + " [32.98511805],\n", + " [26.16131636],\n", + " [20.837328 ],\n", + " [18.08912114],\n", + " [32.44454463],\n", + " [20.40880393],\n", + " [14.77271971],\n", + " [23.59993668],\n", + " [32.20538331],\n", + " [35.10563977],\n", + " [24.68804003],\n", + " [28.10494268],\n", + " [26.01947009],\n", + " [19.28969253],\n", + " [18.83708342],\n", + " [29.59642922],\n", + " [29.11647659],\n", + " [20.50279217],\n", + " [28.59074734],\n", + " [32.05528489],\n", + " [12.6082723 ],\n", + " [30.51435505],\n", + " [24.24119927],\n", + " [22.13846413],\n", + " [24.00480446],\n", + " [23.99405968],\n", + " [19.11200062],\n", + " [29.39328099],\n", + " [21.19803557],\n", + " [26.03808565],\n", + " [11.99838293],\n", + " 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[29.30229721],\n", + " [25.2701313 ],\n", + " [14.37374718],\n", + " [24.38067063],\n", + " [28.23759576],\n", + " [30.41988811],\n", + " [27.67424804],\n", + " [ 9.65967555],\n", + " [28.60455937],\n", + " [15.28458538],\n", + " [ 4.99207237],\n", + " [30.29719902],\n", + " [31.66313775],\n", + " [23.27224396],\n", + " [26.01292048],\n", + " [30.31574759],\n", + " [11.39324839],\n", + " [24.69453743],\n", + " [29.77956043],\n", + " [17.1443272 ],\n", + " [34.77034124],\n", + " [29.11059721],\n", + " [26.22816103],\n", + " [22.3656942 ],\n", + " [24.22134871],\n", + " [23.89958159],\n", + " [30.28771573],\n", + " [12.68705635],\n", + " [26.61958261],\n", + " [30.17734468],\n", + " [25.51299228],\n", + " [23.53424202],\n", + " [19.56670015],\n", + " [32.13235944],\n", + " [23.45345813],\n", + " [18.69029474],\n", + " [26.8489105 ],\n", + " [31.83545044],\n", + " [20.50469471],\n", + " [10.2909952 ],\n", + " [22.8064059 ],\n", + " [33.02304402],\n", + " [14.37908794],\n", + " [32.05051752],\n", + " [11.80869588],\n", + " [34.63529013],\n", + " [15.65682095],\n", + " [28.8399608 ],\n", + " [27.65226934],\n", + " [10.59709376],\n", + " [23.85927886],\n", + " [34.60659275],\n", + " [26.63990859],\n", + " [30.65039152],\n", + " [35.42460583],\n", + " [26.1924373 ],\n", + " [21.29784176],\n", + " [21.40306519],\n", + " [11.61278745],\n", + " [17.63898444],\n", + " [19.45553354],\n", + " [26.0399074 ],\n", + " [20.26468419],\n", + " [19.78737477],\n", + " [30.76662266],\n", + " [26.26024482],\n", + " [17.33934493],\n", + " [22.25865268],\n", + " [26.59842924],\n", + " [21.05390678],\n", + " [15.32835558],\n", + " [21.31277756],\n", + " [32.70684715],\n", + " [30.77919105],\n", + " [30.92291619]])" + ] + }, + "execution_count": 218, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Predicting y_pred on X_train\n", + "auto_y_pred = auto_model.predict(X_train)\n", + "auto_y_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 219, + "metadata": {}, + "outputs": [], + "source": [ + "# Importing the r2 function\n", + "from sklearn.metrics import r2_score" + ] + }, + { + "cell_type": "code", + "execution_count": 220, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2 is: \n", + "\n", + "\n", + "0.8198690008457217\n" + ] + } + ], + "source": [ + "# Calculating the r2\n", + "r2 = r2_score(y_train, auto_y_pred)\n", + "\n", + "print(\"r2 is: \")\n", + "print(\"\\n\")\n", + "print(r2)" + ] + }, + { + "cell_type": "code", + "execution_count": 221, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Our next step is to evaluate the model using the test data. \n", + "\n", + "We would like to ensure that our model is not overfitting the data. This means that our model was made to fit too closely to the training data by being overly complex. If a model is overfitted, it is not generalizable to data outside the training data. In that case, we need to reduce the complexity of the model by removing certain features (variables).\n", + "\n", + "In the cell below, use the model to generate the predicted values for the test data and assign them to `y_test_pred`. Compute the r squared score of the predicted `y_test_pred` and the oberserved `y_test` data." + ] + }, + { + "cell_type": "code", + "execution_count": 222, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[16.93513471],\n", + " [31.44386166],\n", + " [13.92513989],\n", + " [25.33100232],\n", + " [30.86517173],\n", + " [16.12912297],\n", + " [29.84316806],\n", + " [20.79424901],\n", + " [17.51682663],\n", + " [33.5962678 ],\n", + " [15.16484689],\n", + " [24.05681003],\n", + " [13.43861853],\n", + " [30.4849891 ],\n", + " [15.96175622],\n", + " [22.14058281],\n", + " [29.18249959],\n", + " [ 7.26444002],\n", + " [12.34592112],\n", + " [14.11414099],\n", + " [22.31888799],\n", + " [28.78553786],\n", + " [29.70553983],\n", + " [35.29035859],\n", + " [34.53708373],\n", + " [16.04079521],\n", + " [26.86016519],\n", + " [31.90293837],\n", + " [22.70010858],\n", + " [27.39754987],\n", + " [23.10237142],\n", + " [31.48471994],\n", + " [17.0945224 ],\n", + " [20.83682113],\n", + " [27.73349153],\n", + " [28.37221631],\n", + " [27.18683665],\n", + " [27.98877592],\n", + " [26.11857502],\n", + " [11.08719175],\n", + " [17.4036681 ],\n", + " [23.45466615],\n", + " [25.0567287 ],\n", + " [21.985068 ],\n", + " [20.55618207],\n", + " [20.54065738],\n", + " [28.84321132],\n", + " [29.74538267],\n", + " [24.29508899],\n", + " [20.39074464],\n", + " [20.98390062],\n", + " [33.4709278 ],\n", + " [16.27399269],\n", + " [22.37692882],\n", + " [22.76363708],\n", + " [14.67741727],\n", + " [ 7.73764472],\n", + " [24.38242977],\n", + " [19.44958426],\n", + " [30.11714922],\n", + " [16.5131058 ],\n", + " [ 6.38634806],\n", + " [24.59175118],\n", + " [27.26877553],\n", + " [20.30193067],\n", + " [33.77916119],\n", + " [30.9261231 ],\n", + " [24.59543879],\n", + " [26.23317754],\n", + " [22.89863311],\n", + " [25.04347344],\n", + " [27.96854099],\n", + " [25.57556152],\n", + " [11.91524446],\n", + " [26.96353645],\n", + " [30.35319343],\n", + " [28.79986239],\n", + " [13.57871975],\n", + " [21.44849095]])" + ] + }, + "execution_count": 222, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Predicting y_pred on X_test\n", + "y_test_pred = auto_model.predict(X_test)\n", + "y_test_pred" + ] + }, + { + "cell_type": "code", + "execution_count": 223, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r2 is: \n", + "\n", + "\n", + "0.7507754274816081\n" + ] + } + ], + "source": [ + "# Calculating the r2\n", + "r2_test = r2_score(y_test, y_test_pred)\n", + "\n", + "print(\"r2 is: \")\n", + "print(\"\\n\")\n", + "print(r2_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 224, + "metadata": {}, + "outputs": [], "source": [ "# your code here\n" ] @@ -1055,25 +1689,497 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Compute the r squared score for the smaller test set. Is there an improvement in the test r squared?" + "#### Explaining the results\n", + "\n", + "Please, compare the results for the training and test sets and comment. What can you say?" + ] + }, + { + "cell_type": "code", + "execution_count": 225, + "metadata": {}, + "outputs": [], + "source": [ + "# your answer here\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Challenge 5 - Improve the Model Fit\n", + "\n", + "While the most common way to improve the fit of a model is by using [regularization](https://datanice.github.io/machine-learning-101-what-is-regularization-interactive.html), there are other simpler ways to improve model fit. The first is to create a simpler model. The second is to increase the train sample size.\n", + "\n", + "Let us start with the easier option and increase our train sample size to 90% of the data. Create a new test train split and name the new predictors and response variables `X_train09`, `X_test09`, `y_train09`, `y_test09`." + ] + }, + { + "cell_type": "code", + "execution_count": 226, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n", + "X_train09, X_test09, y_train09, y_test09 = train_test_split(X, y, test_size=0.1, random_state=123)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Initialize a new linear regression model. Name this model `auto_model09`. Fit the model to the new sample (training) data." ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 227, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.7913151386161112" + "LinearRegression()" + ] + }, + "execution_count": 227, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# your code here\n", + "auto_model09 = LinearRegression()\n", + "auto_model09.fit(X_train09, y_train09)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute the predicted values and r squared score for our new model and new sample data." + ] + }, + { + "cell_type": "code", + "execution_count": 228, + "metadata": {}, + "outputs": [], + "source": [ + "# your code here\n" + ] + }, + { + "cell_type": "code", + "execution_count": 229, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[17.28581547],\n", + " [23.25345667],\n", + " [24.81188554],\n", + " [22.4177025 ],\n", + " 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[30.59808766],\n", + " [30.69390462]])" ] }, - "execution_count": 40, + "execution_count": 229, "metadata": {}, "output_type": "execute_result" } ], + "source": [ + "auto_y_pred09 = auto_model09.predict(X_train09)\n", + "auto_y_pred09" + ] + }, + { + "cell_type": "code", + "execution_count": 230, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "r209 is: \n", + "\n", + "\n", + "0.8109551916128583\n" + ] + } + ], + "source": [ + "# Calculating the r2\n", + "r2_test09 = r2_score(y_train09, auto_y_pred09)\n", + "\n", + "print(\"r209 is: \")\n", + "print(\"\\n\")\n", + "print(r2_test09)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute the r squared score for the smaller test set. Is there an improvement in the test r squared?" + ] + }, + { + "cell_type": "code", + "execution_count": 231, + "metadata": {}, + "outputs": [], "source": [ "# your code here\n" ] @@ -1091,7 +2197,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 232, "metadata": {}, "outputs": [], "source": [ @@ -1108,7 +2214,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 233, "metadata": {}, "outputs": [], "source": [ @@ -1124,25 +2230,14 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "RFE(estimator=LinearRegression(), n_features_to_select=3)" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 234, "metadata": {}, "outputs": [], "source": [ @@ -1160,7 +2255,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 235, "metadata": {}, "outputs": [], "source": [ @@ -1176,7 +2271,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 236, "metadata": {}, "outputs": [], "source": [ @@ -1199,6 +2294,227 @@ "\n", "* Data integration specialists who are business or content experts but also understand data and programming. This cross-disciplinary track brings together data, technology, and business and will be in high demands in the next decade." ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [], + "source": [ + "'Lab thursday'\n", + "from sklearn.tree import DecisionTreeRegressor\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.tree import plot_tree\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": {}, + "outputs": [], + "source": [ + "model = DecisionTreeRegressor(max_depth=3)\n", + "\n", + "# Not needed because I'm using a decision tree\n", + "#trans = PowerTransform(), StandardScaler()\n", + "#trans.fit(X_train)\n", + "\n", + "model.fit(X_train, y_train)\n", + "\n", + "y_pred_train = model.predict(X_train)\n", + "y_pred_test = model.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": {}, + "outputs": [], + "source": [ + "model1 = DecisionTreeRegressor(max_depth=5)\n", + "\n", + "# Not needed because I'm using a decision tree\n", + "#trans = PowerTransform(), StandardScaler()\n", + "#trans.fit(X_train)\n", + "\n", + "model1.fit(X_train, y_train)\n", + "\n", + "y_pred_train = model1.predict(X_train)\n", + "y_pred_test = model1.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(nrows = 1,ncols = 1,figsize = (4,4), dpi=300)\n", + "\n", + "plot_tree(model,filled = True, rounded=True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 244, + "metadata": {}, + "outputs": [ + { + "ename": "AttributeError", + "evalue": "'numpy.ndarray' object has no attribute 'select_dtypes'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 5\u001b[0m \u001b[0mX_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX_test\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect_dtypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minclude\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'select_dtypes'" + ] + } + ], + "source": [ + "from sklearn.feature_selection import VarianceThreshold # It only works with numerical features\n", + "\n", + "\n", + "X_train = X_train.select_dtypes(include=np.number)\n", + "X_test = X_test.select_dtypes(include=np.number)\n", + "\n", + "#display(X_train)\n", + "print(\"Initial number of numerical columns: \",X_train.shape)\n", + "print()\n", + "\n", + "selector = VarianceThreshold(threshold=10) # Default threshold value is 0\n", + "# Features with a training-set variance lower than this threshold will be removed.\n", + "selector.fit(X_train)\n", + "\n", + "kept_features_indexes = selector.get_support(indices = True) #returns an array of integers corresponding to nonremoved features\n", + "kept_features = list(X_train.iloc[:,kept_features_indexes].columns)\n", + "\n", + "X_train = selector.transform(X_train)\n", + "X_test = selector.transform(X_test)\n", + "\n", + "X_train = pd.DataFrame(X_train, columns=kept_features)\n", + "X_test = pd.DataFrame(X_test, columns=kept_features)\n", + "\n", + "print(\"Final number of numerical columns: \",X_train.shape)\n", + "print()\n", + "X_train" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", + "\n", + "def performance_model(y_train, y_test, y_pred_train, y_pred_test):\n", + "\n", + " # Model validation y_train - y_pred_train\n", + " ME_train = np.mean(y_train-y_pred_train)\n", + " ME_test = np.mean(y_test-y_pred_test)\n", + "\n", + " MAE_train = mean_absolute_error(y_train,y_pred_train)\n", + " MAE_test = mean_absolute_error(y_test,y_pred_test)\n", + "\n", + " MSE_train = mean_squared_error(y_train,y_pred_train)\n", + " MSE_test = mean_squared_error(y_test,y_pred_test)\n", + "\n", + " RMSE_train = np.sqrt(MSE_train)\n", + " RMSE_test = np.sqrt(MSE_test)\n", + "\n", + " MAPE_train = np.mean((np.abs(y_train-y_pred_train) / y_train)* 100.)\n", + " MAPE_test = np.mean((np.abs(y_test-y_pred_test) / y_test)* 100.)\n", + "\n", + " R2_train = r2_score(y_train,y_pred_train)\n", + " R2_test = r2_score(y_test,y_pred_test)\n", + "\n", + " performance = pd.DataFrame({'Error_metric': ['Mean error','Mean absolute error','Mean squared error',\n", + " 'Root mean squared error','Mean absolute percentual error',\n", + " 'R2'],\n", + " 'Train': [ME_train, MAE_train, MSE_train, RMSE_train, MAPE_train, R2_train],\n", + " 'Test' : [ME_test, MAE_test , MSE_test, RMSE_test, MAPE_test, R2_test]})\n", + "\n", + " pd.options.display.float_format = '{:.2f}'.format\n", + "\n", + "\n", + " df_train = pd.DataFrame({'Real_value': y_train, 'Predicted_value': y_pred_train})\n", + " df_test = pd.DataFrame({'Real_value': y_test, 'PRedicted_value': y_pred_test})\n", + "\n", + " return performance, df_train, df_test" + ] + }, + { + "cell_type": "code", + "execution_count": 243, + "metadata": {}, + "outputs": [ + { + "ename": "Exception", + "evalue": "Data must be 1-dimensional", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mException\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mperformance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mperformance_model\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mperformance\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mperformance_model\u001b[0;34m(y_train, y_test, y_pred_train, y_pred_test)\u001b[0m\n\u001b[1;32m 31\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 33\u001b[0;31m \u001b[0mdf_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'Real_value'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'Predicted_value'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my_pred_train\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 34\u001b[0m \u001b[0mdf_test\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'Real_value'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'PRedicted_value'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0my_pred_test\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 35\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m 466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 468\u001b[0;31m \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minit_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/internals/construction.py\u001b[0m in \u001b[0;36m_homogenize\u001b[0;34m(data, index, dtype)\u001b[0m\n\u001b[1;32m 349\u001b[0m \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 350\u001b[0m \u001b[0mval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfast_multiget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0msubarr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 495\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 496\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Data must be 1-dimensional\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 497\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 498\u001b[0m \u001b[0msubarr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray_tuplesafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mException\u001b[0m: Data must be 1-dimensional" + ] + } + ], + "source": [ + "performance, _ ,_ = performance_model(y_train, y_test, y_pred_train, y_pred_test)\n", + "performance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -1217,7 +2533,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.3" + "version": "3.8.5" } }, "nbformat": 4,