[YCMI_CBDS_Summer_Course]_Exercises_Machine_Learning_and_Bioinformatics_Laboratory_Session_Warfarin.ipynb

{"nbformat":4,"nbformat_minor":0,"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":"3.6.5"},"colab":{"name":"[YCMI_CBDS_Summer_Course]_Exercises_Machine_Learning_and_Bioinformatics_Laboratory_Session_Warfarin.ipynb","provenance":[],"collapsed_sections":[]}},"cells":[{"cell_type":"markdown","metadata":{"id":"6Csf30RyxlDt","colab_type":"text"},"source":["# Lab Assignment: (A) Predictive Modeling of Drug Dosage"]},{"cell_type":"markdown","metadata":{"id":"YLfcxvywxlD2","colab_type":"text"},"source":["In this exercise, you will learn to predict the most likely efficacious drug dosage based on several patient factors, such as height, weight, age and the genotypes at certain genes. This exercise is based on the results of The International Warfarin Pharmacogenetics Consortium __2009__, \"Estimation of the Warfarin Dose with Clinical and Pharmacogenetic Data\", _N. Engl. J. Med_, 360, Pgs. 753-764 (data was downloaded from https://www.pharmgkb.org/downloads). The authors create a model for warfarin dosage predictions based on clinical data and the genotypes of individuals at two genes. \n","\n","The lab assignment will explore preprocessing of the data, the creation of a predictive model based on linear regression and regularization "]},{"cell_type":"markdown","metadata":{"id":"QjWKyvRdxlD7","colab_type":"text"},"source":["## Preprocessing of the data"]},{"cell_type":"code","metadata":{"id":"rm5eDrLCxlD-","colab_type":"code","colab":{}},"source":["from sklearn import preprocessing,decomposition,linear_model\n","import plotnine as p9\n","import pandas as pd"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"Ou0psI4-SA7p","colab_type":"code","outputId":"ef20a8af-118c-414b-d11c-4950628dbc37","colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["#google drive connection\n","from google.colab import drive\n","drive.mount(\"/content/gdrive\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"pbWL-I17xlEN","colab_type":"text"},"source":["### Examine the data\n","First, take a look at the original data file to get a sense of the unprocessed data columns."]},{"cell_type":"code","metadata":{"id":"PX-Jk8ztxlEQ","colab_type":"code","outputId":"d82a8a6c-ee98-4d31-a692-f98758db0bfd","colab":{"base_uri":"https://localhost:8080/","height":462}},"source":["data_unprocessed = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/Warfarin_Study.txt\", sep=\"\\t\")\n","data_unprocessed.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","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>PharmGKB Subject ID</th>\n","      <th>Gender</th>\n","      <th>Race (Reported)</th>\n","      <th>Race (OMB)</th>\n","      <th>Project Site</th>\n","      <th>Ethnicity (OMB)</th>\n","      <th>Age</th>\n","      <th>Height (cm)</th>\n","      <th>Weight (kg)</th>\n","      <th>Comorbidities</th>\n","      <th>Carbamazepine (Tegretol)</th>\n","      <th>Simvastatin (Zocor)</th>\n","      <th>Amiodarone (Cordarone)</th>\n","      <th>Target INR</th>\n","      <th>Subject Reached Stable Dose of Warfarin</th>\n","      <th>Therapeutic Dose of Warfarin</th>\n","      <th>INR on Reported Therapeutic Dose of Warfarin</th>\n","      <th>Current Smoker</th>\n","      <th>CYP2C9 consensus</th>\n","      <th>VKORC1 -1639 consensus</th>\n","      <th>Phenytoin (Dilantin)</th>\n","      <th>Rifampin or Rifampicin</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>PA135312261</td>\n","      <td>male</td>\n","      <td>White</td>\n","      <td>White</td>\n","      <td>1</td>\n","      <td>not Hispanic or Latino</td>\n","      <td>60 - 69</td>\n","      <td>193.04</td>\n","      <td>115.7</td>\n","      <td>No Cancer</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>2.5</td>\n","      <td>1.0</td>\n","      <td>49.0</td>\n","      <td>2.60</td>\n","      <td>0.0</td>\n","      <td>*1/*1</td>\n","      <td>A/G</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>PA135312262</td>\n","      <td>female</td>\n","      <td>White</td>\n","      <td>White</td>\n","      <td>1</td>\n","      <td>not Hispanic or Latino</td>\n","      <td>50 - 59</td>\n","      <td>176.53</td>\n","      <td>144.2</td>\n","      <td>Cancer</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>2.5</td>\n","      <td>1.0</td>\n","      <td>42.0</td>\n","      <td>2.15</td>\n","      <td>1.0</td>\n","      <td>*1/*1</td>\n","      <td>A/A</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>PA135312263</td>\n","      <td>female</td>\n","      <td>White</td>\n","      <td>White</td>\n","      <td>1</td>\n","      <td>not Hispanic or Latino</td>\n","      <td>40 - 49</td>\n","      <td>162.56</td>\n","      <td>77.1</td>\n","      <td>No Cancer</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>2.5</td>\n","      <td>1.0</td>\n","      <td>53.0</td>\n","      <td>1.90</td>\n","      <td>0.0</td>\n","      <td>*1/*1</td>\n","      <td>G/G</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>PA135312264</td>\n","      <td>male</td>\n","      <td>White</td>\n","      <td>White</td>\n","      <td>1</td>\n","      <td>not Hispanic or Latino</td>\n","      <td>60 - 69</td>\n","      <td>182.24</td>\n","      <td>90.7</td>\n","      <td>No Cancer</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>2.5</td>\n","      <td>1.0</td>\n","      <td>28.0</td>\n","      <td>2.40</td>\n","      <td>0.0</td>\n","      <td>*1/*1</td>\n","      <td>A/G</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>PA135312265</td>\n","      <td>male</td>\n","      <td>White</td>\n","      <td>White</td>\n","      <td>1</td>\n","      <td>not Hispanic or Latino</td>\n","      <td>50 - 59</td>\n","      <td>167.64</td>\n","      <td>72.6</td>\n","      <td>No Cancer</td>\n","      <td>NaN</td>\n","      <td>0.0</td>\n","      <td>0.0</td>\n","      <td>2.5</td>\n","      <td>1.0</td>\n","      <td>42.0</td>\n","      <td>1.90</td>\n","      <td>0.0</td>\n","      <td>*1/*3</td>\n","      <td>A/G</td>\n","      <td>NaN</td>\n","      <td>NaN</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["  PharmGKB Subject ID  Gender  ... Phenytoin (Dilantin) Rifampin or Rifampicin\n","0         PA135312261    male  ...                  NaN                    NaN\n","1         PA135312262  female  ...                  NaN                    NaN\n","2         PA135312263  female  ...                  NaN                    NaN\n","3         PA135312264    male  ...                  NaN                    NaN\n","4         PA135312265    male  ...                  NaN                    NaN\n","\n","[5 rows x 22 columns]"]},"metadata":{"tags":[]},"execution_count":3}]},{"cell_type":"markdown","metadata":{"id":"S6fx5sFDxlEb","colab_type":"text"},"source":["Next, examine the processed data file."]},{"cell_type":"code","metadata":{"id":"94FIzzmrxlEe","colab_type":"code","outputId":"99b7eb05-38dc-44dd-891b-38343f56da7c","colab":{"base_uri":"https://localhost:8080/","height":224}},"source":["data_processed = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/Warfarin_Study_train.txt\", sep=\"\\t\")\n","data_processed.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","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>Therapeutic.Dose.of.Warfarin</th>\n","      <th>Height..cm.</th>\n","      <th>Weight..kg.</th>\n","      <th>Age_Decades</th>\n","      <th>Amiodarone..Cordarone.</th>\n","      <th>EnzymeInducer</th>\n","      <th>Asian</th>\n","      <th>AA</th>\n","      <th>Unk</th>\n","      <th>VKORC1_G_A</th>\n","      <th>VKORC1_A_A</th>\n","      <th>VKORC1_Missing</th>\n","      <th>CYP2C9_1_2</th>\n","      <th>CYP2C9_1_3</th>\n","      <th>CYP2C9_2_2</th>\n","      <th>CYP2C9_2_3</th>\n","      <th>CYP2C9_3_3</th>\n","      <th>CYP2C9_Missing</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>23.0</td>\n","      <td>160.02</td>\n","      <td>63.5</td>\n","      <td>6</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>49.0</td>\n","      <td>181.61</td>\n","      <td>108.9</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>30.0</td>\n","      <td>173.99</td>\n","      <td>79.4</td>\n","      <td>7</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>1</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>56.0</td>\n","      <td>172.72</td>\n","      <td>88.0</td>\n","      <td>2</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>51.0</td>\n","      <td>182.88</td>\n","      <td>104.3</td>\n","      <td>5</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   Therapeutic.Dose.of.Warfarin  Height..cm.  ...  CYP2C9_3_3  CYP2C9_Missing\n","0                          23.0       160.02  ...           0               0\n","1                          49.0       181.61  ...           0               0\n","2                          30.0       173.99  ...           0               0\n","3                          56.0       172.72  ...           0               0\n","4                          51.0       182.88  ...           0               0\n","\n","[5 rows x 18 columns]"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"markdown","metadata":{"id":"npYK5mWGxlEo","colab_type":"text"},"source":["Note that many of the columns have been processed already in the interests of time. Especially pay attention to the recoding of the categorical variables associated with the genotypes. For example, the rs9923231 SNP associated with gene _VKORC1_ encodes a G -> A mutation, and so there are four possible genotypic categories (with no associated quantitative measure): G/G, G/A, A/A, Missing. If the non-mutated, homozygous state G/G is treated as the reference state, the others are encoded by a so-called \"one-hot\" enoding strategy. In this encoding, for $\\textit{N}$ categories, $\\textit{N-1}$ new variables are created for all the non-reference states; when a category is observed, the corresponding variable has a value of 1, while all the others are set to 0. When the reference category is observed, all the variables are set to 0.  \n"]},{"cell_type":"markdown","metadata":{"id":"o4XYGaFyxlEq","colab_type":"text"},"source":["### Shifting and Scaling of the data\n","\n","In this step, you will shift and scale the data in all the continuous-valued columns by two different transformations:\n","\n","First try \"Standard Scaling\":\n","\\begin{equation*} x_{i} \\to \\frac{x_{i} - \\mu}{\\sigma}, \\end{equation*} where $\\mu$ is the mean and $\\sigma$ is the standard deviation of the data. This is done to ensure that the scales of all the expression values are about the same, and thus prevents biases in the weight attached to a given variable, simply because its magnitude happens to be larger or smaller on average. These shifted and rescaled values are the so-called Z-scores of the data.\n","\n","Next, try \"MinMax Scaling\":\n","\\begin{equation*} x_{i} \\to \\frac{x_{i} - min(x_{i})}{max(x_{i}) - min(x_{i})}, \\end{equation*} where $max(x_{i})$ is the maximum value of the data series and $min(x_{i})$ is the minimum value of the data series.\n","\n","These are two different ways of scaling. The \"standardization\" procedure in the first method is very commonly used, but distorts the distribution a little bit. The min-max scaling procedure retains the distribution, and confines the range to be within 0 and 1. There is no definitive prescription for what is best, so trying both can be helpful. "]},{"cell_type":"markdown","metadata":{"id":"Q_fdgdi3qsYi","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"2vuibABTMFIn","colab_type":"code","colab":{}},"source":["# load the training data, with the y matrix being assigned the 'Therapeutic.Dose.of.Warfarin' data, and the x matrix being assigned\n","# all the other columns except for 'Therapeutic.Dose.of.Warfarin' \n","trainset = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/Warfarin_Study_train.txt\", sep=\"\\t\")\n","y_train = \n","x_train = \n","\n","# load the test data, with the y matrix being assigned the 'Therapeutic.Dose.of.Warfarin' data, and the x matrix being assigned\n","# all the other columns except for 'Therapeutic.Dose.of.Warfarin' \n","testset = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/Warfarin_Study_test.txt\",sep=\"\\t\")\n","y_test = \n","x_test = \n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3_x9vtpQrcOU","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"GXAKqxwlOQZT","colab_type":"code","colab":{}},"source":["#### Create a 'StandardScaler()' object\n","####------>\n","scaler_obj = \n","\n","# Create a list of the columns you want to normalize\n","to_normalize = [**Enter here**]\n","\n","####Here we create a separate copy of the original training and test data, so that any modifications\n","#### to the new copy are not transferred back to the original matrix \n","####(this would happen if we set the x_ss_train = x_train)\n","x_ss_train = x_train.copy()\n","\n","####Select out only those columns that you want to normalize and run the 'fit_transform' function on them\n","#### for both the test and train functions; the results from both those columns should be returned back to the same columns\n","x_ss_train[to_normalize] = \n","\n","x_ss_test = x_test.copy()\n","x_ss_test[to_normalize] = "],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"CM5tWHLbsJ8B","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"WrvMUZvdxlEt","colab_type":"code","colab":{}},"source":["#### Run MinMax Scaling\n","minmax_obj = \n","x_mm_train = x_train.copy()\n","x_mm_train[to_normalize] = \n","\n","x_mm_test = x_test.copy()\n","x_mm_test[to_normalize] = "],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"DVmi6zsHxlE3","colab_type":"text"},"source":["### Linear regression model"]},{"cell_type":"markdown","metadata":{"id":"KMjhENH_xlE5","colab_type":"text"},"source":["#### Unscaled data"]},{"cell_type":"markdown","metadata":{"id":"dtpmUomisSsm","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"lZg4Dg2KRyK_","colab_type":"code","outputId":"fa2af579-d2af-4c77-fa1b-93c1ac106b6d","colab":{"base_uri":"https://localhost:8080/","height":86}},"source":["#### Create a 'LinearRegression()' object from the 'linear_model' class\n","regr = \n","\n","####Fit the x_train_arr data to the square root of the y_train_arr data using the 'fit' function\n","####In this case, we chose to take the square root of the target output data to match the\n","#### method of the original paper; in general, you may consider transforming your target data and feature \n","#### data in different ways to better fit the model\n","####------>\n","\n","\n","####Print out the fit coefficients and intercept\n","print(regr.coef_)\n","print(regr.intercept_)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[ 0.01187106  0.01317074 -0.23686162 -0.57034589  1.16519373 -0.16687181\n"," -0.15441854 -0.18021427 -0.84610236 -1.68836373 -0.63966833 -0.48231671\n"," -0.8303828  -0.99716393 -1.93056185 -2.09245817 -0.49839203]\n","4.903674669504408\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"2-K-E14YsmKO","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"dSx6z4LJSPzH","colab_type":"code","outputId":"a6c04358-65c7-4acc-b642-8392fc431599","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["#### Test the model by finding the score using the 'score' function; remember to use the square root of \n","####the test set\n","####------>\n","regression_score = \n","print(regression_score)\n","\n","####Find the prediction values of the model explicitly; this step is mainly for the plotting function below.\n","y_predict = regr.predict(x_test)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.429307232660645\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"75kuZQNPxlFL","colab_type":"text"},"source":["#### Standard Scaling"]},{"cell_type":"markdown","metadata":{"id":"9sJYOb1Jsz76","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"oCfQMS_OS3Jg","colab_type":"code","outputId":"3c25e821-b87f-4a10-e8f6-ab9dce3c3d91","colab":{"base_uri":"https://localhost:8080/","height":86}},"source":["#### Create a 'LinearRegression()' object from the 'linear_model' class\n","regr = \n","\n","#### Fit the x_train_arr data to the square root of the y_train_arr data using the 'fit' function\n","#### In this case, we chose to take the square root of the target output data to match the\n","#### method of the original paper; in general, you may consider transforming your target data and feature \n","#### data in different ways to better fit the model\n","####------>\n","\n","\n","####Print out the fit coefficients and intercept\n","print(regr.coef_)\n","print(regr.intercept_)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[ 0.12830048  0.29267718 -0.34494393 -0.57034589  1.16519373 -0.16687181\n"," -0.15441854 -0.18021427 -0.84610236 -1.68836373 -0.63966833 -0.48231671\n"," -0.8303828  -0.99716393 -1.93056185 -2.09245817 -0.49839203]\n","6.526938003775576\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"0UUhjppPs3wB","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"ybSqd4foxlFU","colab_type":"code","outputId":"b5e0ea78-6891-4880-e785-6410b4622794","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["#### Test the model by finding the score using the 'score' function; remember to use the square root of \n","####the test set\n","####------>\n","regression_score = \n","print(regression_score)\n","\n","####Find the prediction values of the model explicitly; this step is mainly for the plotting function below. \n","y_ss_predict = regr.predict(x_ss_test)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.4290141910849038\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"wj1j43NPxlFb","colab_type":"text"},"source":["#### **(Optional)** MinMax Scaling"]},{"cell_type":"code","metadata":{"id":"lTs3xGQdxlFe","colab_type":"code","outputId":"291843f4-83ea-425b-fe65-be3548a2b7e8","colab":{"base_uri":"https://localhost:8080/","height":86}},"source":["#### Create a 'LinearRegression()' object from the 'linear_model' class\n","regr = \n","\n","####Fit the x_train_arr data to the square root of the y_train_arr data using the 'fit' function\n","####In this case, we chose to take the square root of the target output data to match the\n","#### method of the original paper; in general, you may consider transforming your target data and feature \n","#### data in different ways to better fit the model\n","\n","\n","\n","####Print out the fit coefficients and intercept\n","print(regr.coef_)\n","print(regr.intercept_)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["[ 0.91442811  2.7355627  -1.89489293 -0.57034589  1.16519373 -0.16687181\n"," -0.15441854 -0.18021427 -0.84610236 -1.68836373 -0.63966833 -0.48231671\n"," -0.8303828  -0.99716393 -1.93056185 -2.09245817 -0.49839203]\n","6.545462209702442\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7pu9n7S3xlFl","colab_type":"code","outputId":"b1d43102-4bb0-42d0-de74-d443a78c7189","colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["#### Test the model by finding the score using the 'score' function; remember to use the square root of \n","####the test set\n","####------>\n","regression_score = \n","print(regression_score)\n","\n","####Find the prediction values of the model explicitly; this step is mainly for the plotting function below. \n","y_mm_predict = regr.predict(x_mm_test)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["0.4117745843836391\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"eOVn0c5dxlFr","colab_type":"text"},"source":["### Plot the results: first plot the fit to the original dosage, without the square root transformation; then plot the fit to the transformed dosage data."]},{"cell_type":"markdown","metadata":{"id":"zCYjvT0Ta-Yc","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"maD16DOFV26V","colab_type":"code","outputId":"3d2fa7d6-69d5-4aeb-e040-b829186b70c2","colab":{"base_uri":"https://localhost:8080/","height":527}},"source":["#In the space marked by 'Enter here', create a dataframe with one column being the square of y_predict, and the other being y_test\n","(\n","    p9.ggplot(**Enter here**)\n","      + p9.geom_point(p9.aes(x=\"y_predict**2\",y=\"y_test\"), color=\"black\")\n","      + p9.geom_abline(intercept=0, slope=1, color='blue')\n","      + p9.ggtitle('Weekly dosage')\n","      + p9.xlim(0, 120)\n","      + p9.ylim(0, 120)\n",")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/plotnine/layer.py:452: PlotnineWarning: geom_point : Removed 3 rows containing missing values.\n","  self.data = self.geom.handle_na(self.data)\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"execute_result","data":{"text/plain":["<ggplot: (8787019805995)>"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"markdown","metadata":{"id":"4qirf6UEbJNY","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"OWyzRYpBX2K_","colab_type":"code","outputId":"bd22ca82-2f3c-4803-a52e-63267e1dee44","colab":{"base_uri":"https://localhost:8080/","height":492}},"source":["#In the space marked by 'Enter here', create a dataframe with one column being y_predict, and the other being the square root of y_test\n","(\n","    p9.ggplot(**Enter here**)\n","      + p9.geom_point(p9.aes(x=\"y_predict\",y=\"y_test**(1./2)\"), color=\"black\")\n","      + p9.geom_abline(intercept=0, slope=1, color='blue')\n","      + p9.ggtitle('Square Root of Weekly dosage')\n","      + p9.xlim(0, 18)\n","      + p9.ylim(0, 18)\n",")"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"execute_result","data":{"text/plain":["<ggplot: (-9223363249835328964)>"]},"metadata":{"tags":[]},"execution_count":18}]},{"cell_type":"markdown","metadata":{"id":"kpUW7qE8xlF0","colab_type":"text"},"source":["# Lab Assignment: (B) Multiclass Classification"]},{"cell_type":"markdown","metadata":{"id":"xdGdlsdbxlF2","colab_type":"text"},"source":["We will now explore a multiclass classification problem using data from PsychENCODE.  The following example is based on RNAseq gene expression data from post-mortem Prefrontal Cortex samples of control subjects, and subjects with Schizophrenia, Bipolar disorder and Autism."]},{"cell_type":"markdown","metadata":{"id":"CaXksjpyxlF3","colab_type":"text"},"source":["## Import the necessary libraries"]},{"cell_type":"code","metadata":{"id":"9svzklZxxlF4","colab_type":"code","colab":{}},"source":["from sklearn.ensemble import RandomForestClassifier"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"zeXRcBU8xlF-","colab_type":"text"},"source":["## Load the PsychENCODE data"]},{"cell_type":"code","metadata":{"id":"mi0z54JTxlGA","colab_type":"code","colab":{}},"source":["train = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/pec_traindata.csv\", header=None)\n","test = pd.read_csv(\"/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/pec_testdata.csv\", header=None)"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"tpm6INkVxlGE","colab_type":"text"},"source":["## Extract training data, and learn a Random Forest classifier\n","\n","The training and test data matrices each contain 200 predictor genes (columns 0-199) and class labels 0-3 in the final column.  Classes are: 0 = Controls, 1 = Schizophrenia, 2 = Bipolar, 3 = Autism.  15 subjects from each class are included in training and testing sets.  Use a forest with 20 trees of maximum depth 4."]},{"cell_type":"code","metadata":{"id":"K3QO91mzY3NH","colab_type":"code","outputId":"9e5858f6-175b-441d-be9e-fac5c6db3d88","colab":{"base_uri":"https://localhost:8080/","height":253}},"source":["test.head()"],"execution_count":0,"outputs":[{"output_type":"execute_result","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>0</th>\n","      <th>1</th>\n","      <th>2</th>\n","      <th>3</th>\n","      <th>4</th>\n","      <th>5</th>\n","      <th>6</th>\n","      <th>7</th>\n","      <th>8</th>\n","      <th>9</th>\n","      <th>10</th>\n","      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     <th>176</th>\n","      <th>177</th>\n","      <th>178</th>\n","      <th>179</th>\n","      <th>180</th>\n","      <th>181</th>\n","      <th>182</th>\n","      <th>183</th>\n","      <th>184</th>\n","      <th>185</th>\n","      <th>186</th>\n","      <th>187</th>\n","      <th>188</th>\n","      <th>189</th>\n","      <th>190</th>\n","      <th>191</th>\n","      <th>192</th>\n","      <th>193</th>\n","      <th>194</th>\n","      <th>195</th>\n","      <th>196</th>\n","      <th>197</th>\n","      <th>198</th>\n","      <th>199</th>\n","      <th>200</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.729270</td>\n","      <td>-0.61931</td>\n","      <td>-0.681020</td>\n","      <td>-1.114600</td>\n","      <td>-0.355030</td>\n","      <td>-0.10202</td>\n","      <td>0.086386</td>\n","      <td>-0.48257</td>\n","      <td>0.3898</td>\n","      <td>1.332500</td>\n","      <td>-1.08380</td>\n","      <td>0.50161</td>\n","      <td>0.20778</td>\n","      <td>0.938810</td>\n","      <td>1.08730</td>\n","      <td>0.22504</td>\n","      <td>0.83498</td>\n","      <td>1.22800</td>\n","      <td>-1.64790</td>\n","      <td>0.32000</td>\n","      <td>0.59590</td>\n","      <td>0.57817</td>\n","      <td>0.48111</td>\n","      <td>1.585600</td>\n","      <td>0.87424</td>\n","      <td>-0.30298</td>\n","      <td>0.97993</td>\n","      <td>0.23303</td>\n","      <td>0.3385</td>\n","      <td>0.13927</td>\n","      <td>-0.18925</td>\n","      <td>0.86383</td>\n","      <td>-0.83040</td>\n","      <td>-0.2739</td>\n","      <td>-2.143300</td>\n","      <td>2.41980</td>\n","      <td>-1.055000</td>\n","      <td>0.261800</td>\n","      <td>-0.56595</td>\n","      <td>-0.20645</td>\n","      <td>...</td>\n","      <td>1.543700</td>\n","      <td>0.57511</td>\n","      <td>0.305700</td>\n","      <td>1.33720</td>\n","      <td>0.93881</td>\n","      <td>-0.54025</td>\n","      <td>1.54150</td>\n","      <td>1.51650</td>\n","      <td>0.765290</td>\n","      <td>-0.90410</td>\n","      <td>1.558800</td>\n","      <td>-0.75835</td>\n","      <td>-0.65668</td>\n","      <td>0.20645</td>\n","      <td>0.164210</td>\n","      <td>0.58432</td>\n","      <td>1.00430</td>\n","      <td>0.089642</td>\n","      <td>-0.99471</td>\n","      <td>0.60133</td>\n","      <td>0.69499</td>\n","      <td>0.18595</td>\n","      <td>-0.02076</td>\n","      <td>-0.233700</td>\n","      <td>0.86666</td>\n","      <td>0.37027</td>\n","      <td>-0.17738</td>\n","      <td>-0.66152</td>\n","      <td>0.55532</td>\n","      <td>0.36472</td>\n","      <td>0.28132</td>\n","      <td>-0.40524</td>\n","      <td>0.023356</td>\n","      <td>-0.55835</td>\n","      <td>0.21441</td>\n","      <td>0.41441</td>\n","      <td>-0.39821</td>\n","      <td>1.1243</td>\n","      <td>0.631140</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>-2.060600</td>\n","      <td>0.81312</td>\n","      <td>-0.127490</td>\n","      <td>1.684300</td>\n","      <td>-0.791640</td>\n","      <td>-1.28070</td>\n","      <td>-1.154100</td>\n","      <td>-1.38980</td>\n","      <td>-1.0826</td>\n","      <td>-0.049322</td>\n","      <td>-0.25308</td>\n","      <td>-0.65990</td>\n","      <td>-0.48111</td>\n","      <td>-0.433580</td>\n","      <td>1.04040</td>\n","      <td>-0.13338</td>\n","      <td>0.89148</td>\n","      <td>-1.31840</td>\n","      <td>0.89535</td>\n","      <td>-0.78191</td>\n","      <td>2.37400</td>\n","      <td>-0.96846</td>\n","      <td>-0.51194</td>\n","      <td>2.419800</td>\n","      <td>-0.82765</td>\n","      <td>1.43550</td>\n","      <td>0.16355</td>\n","      <td>-0.34813</td>\n","      <td>1.0932</td>\n","      <td>-1.14280</td>\n","      <td>0.84421</td>\n","      <td>1.75840</td>\n","      <td>0.56900</td>\n","      <td>1.5458</td>\n","      <td>-0.053220</td>\n","      <td>-0.11312</td>\n","      <td>0.376530</td>\n","      <td>0.498670</td>\n","      <td>0.52232</td>\n","      <td>-1.52890</td>\n","      <td>...</td>\n","      <td>-0.010380</td>\n","      <td>-1.11950</td>\n","      <td>0.033739</td>\n","      <td>-0.61695</td>\n","      <td>-1.01190</td>\n","      <td>1.34680</td>\n","      <td>-0.13273</td>\n","      <td>-1.25600</td>\n","      <td>-0.667990</td>\n","      <td>-1.75240</td>\n","      <td>1.802700</td>\n","      <td>0.25174</td>\n","      <td>0.74801</td>\n","      <td>-0.33438</td>\n","      <td>0.018814</td>\n","      <td>0.51712</td>\n","      <td>1.04600</td>\n","      <td>-0.596670</td>\n","      <td>-0.34262</td>\n","      <td>1.52890</td>\n","      <td>-0.98729</td>\n","      <td>-0.80951</td>\n","      <td>-1.09670</td>\n","      <td>-0.892450</td>\n","      <td>-2.31330</td>\n","      <td>0.81132</td>\n","      <td>-0.31864</td>\n","      <td>1.47290</td>\n","      <td>-1.02280</td>\n","      <td>1.82970</td>\n","      <td>0.73605</td>\n","      <td>-1.37630</td>\n","      <td>-1.081400</td>\n","      <td>-0.77927</td>\n","      <td>1.20890</td>\n","      <td>-1.14280</td>\n","      <td>1.04040</td>\n","      <td>0.4293</td>\n","      <td>-0.097461</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.620090</td>\n","      <td>1.31690</td>\n","      <td>-0.324790</td>\n","      <td>-0.385600</td>\n","      <td>-0.831310</td>\n","      <td>-1.20220</td>\n","      <td>-0.870440</td>\n","      <td>-1.11950</td>\n","      <td>1.7061</td>\n","      <td>0.664750</td>\n","      <td>-0.48403</td>\n","      <td>-0.62876</td>\n","      <td>0.34124</td>\n","      <td>-0.666370</td>\n","      <td>-0.20049</td>\n","      <td>-0.33712</td>\n","      <td>0.85351</td>\n","      <td>0.15895</td>\n","      <td>0.17211</td>\n","      <td>-0.36541</td>\n","      <td>1.02060</td>\n","      <td>-0.88091</td>\n","      <td>-1.07680</td>\n","      <td>0.775760</td>\n","      <td>-0.88763</td>\n","      <td>-1.41930</td>\n","      <td>-1.76450</td>\n","      <td>-0.42788</td>\n","      <td>1.1146</td>\n","      <td>-1.05280</td>\n","      <td>1.36300</td>\n","      <td>0.98413</td>\n","      <td>-0.16290</td>\n","      <td>1.1158</td>\n","      <td>-0.101370</td>\n","      <td>-1.26320</td>\n","      <td>-0.263810</td>\n","      <td>-0.317270</td>\n","      <td>-0.37932</td>\n","      <td>-1.71730</td>\n","      <td>...</td>\n","      <td>2.235800</td>\n","      <td>-1.11220</td>\n","      <td>-1.585600</td>\n","      <td>1.80940</td>\n","      <td>-1.07680</td>\n","      <td>1.04940</td>\n","      <td>1.18370</td>\n","      <td>-1.17720</td>\n","      <td>-0.062317</td>\n","      <td>-0.45218</td>\n","      <td>0.654270</td>\n","      <td>-0.64386</td>\n","      <td>-2.95710</td>\n","      <td>-0.66313</td>\n","      <td>0.522320</td>\n","      <td>0.17013</td>\n","      <td>0.69747</td>\n","      <td>-0.344000</td>\n","      <td>-1.39320</td>\n","      <td>-0.94792</td>\n","      <td>-0.89148</td>\n","      <td>-1.15410</td>\n","      <td>0.76442</td>\n","      <td>0.648650</td>\n","      <td>-1.41580</td>\n","      <td>0.24973</td>\n","      <td>1.51650</td>\n","      <td>-0.25576</td>\n","      <td>-0.27525</td>\n","      <td>-1.11830</td>\n","      <td>-0.98941</td>\n","      <td>-1.16170</td>\n","      <td>-1.069900</td>\n","      <td>0.38141</td>\n","      <td>1.32460</td>\n","      <td>-1.05620</td>\n","      <td>1.53730</td>\n","      <td>1.4301</td>\n","      <td>-0.734350</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.498670</td>\n","      <td>-0.38281</td>\n","      <td>-0.109200</td>\n","      <td>-0.098113</td>\n","      <td>-1.095500</td>\n","      <td>-1.69510</td>\n","      <td>-0.602890</td>\n","      <td>-0.48111</td>\n","      <td>1.0140</td>\n","      <td>0.053869</td>\n","      <td>-0.70744</td>\n","      <td>-0.46226</td>\n","      <td>-2.27720</td>\n","      <td>-1.541500</td>\n","      <td>-0.69913</td>\n","      <td>-0.32889</td>\n","      <td>-0.23837</td>\n","      <td>-1.67360</td>\n","      <td>-0.12488</td>\n","      <td>0.42859</td>\n","      <td>0.74801</td>\n","      <td>-1.38980</td>\n","      <td>-0.37444</td>\n","      <td>-0.022707</td>\n","      <td>-0.56291</td>\n","      <td>0.62797</td>\n","      <td>-1.08840</td>\n","      <td>-0.58278</td>\n","      <td>1.7117</td>\n","      <td>-1.56540</td>\n","      <td>-0.14255</td>\n","      <td>-0.12488</td>\n","      <td>-0.82765</td>\n","      <td>0.4601</td>\n","      <td>0.883790</td>\n","      <td>-1.73460</td>\n","      <td>0.098765</td>\n","      <td>0.035038</td>\n","      <td>-1.14660</td>\n","      <td>-1.30010</td>\n","      <td>...</td>\n","      <td>0.024654</td>\n","      <td>-0.23170</td>\n","      <td>-1.263200</td>\n","      <td>0.69087</td>\n","      <td>-1.35000</td>\n","      <td>0.11704</td>\n","      <td>2.06060</td>\n","      <td>0.31114</td>\n","      <td>0.795190</td>\n","      <td>0.73776</td>\n","      <td>-0.050621</td>\n","      <td>-0.14976</td>\n","      <td>-1.83670</td>\n","      <td>-0.77576</td>\n","      <td>0.758350</td>\n","      <td>-0.77576</td>\n","      <td>-0.59049</td>\n","      <td>-0.457940</td>\n","      <td>-0.76877</td>\n","      <td>1.40350</td>\n","      <td>-1.43550</td>\n","      <td>0.27727</td>\n","      <td>-0.51490</td>\n","      <td>-0.864770</td>\n","      <td>-1.36800</td>\n","      <td>1.42650</td>\n","      <td>0.90312</td>\n","      <td>1.26320</td>\n","      <td>0.52083</td>\n","      <td>1.34360</td>\n","      <td>-0.88379</td>\n","      <td>1.02610</td>\n","      <td>-1.528900</td>\n","      <td>-0.60289</td>\n","      <td>0.39120</td>\n","      <td>0.93680</td>\n","      <td>0.63114</td>\n","      <td>1.1668</td>\n","      <td>-2.419800</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>-0.081829</td>\n","      <td>1.93660</td>\n","      <td>0.064917</td>\n","      <td>-0.447160</td>\n","      <td>-0.068818</td>\n","      <td>-0.95200</td>\n","      <td>0.254420</td>\n","      <td>-0.79697</td>\n","      <td>2.0943</td>\n","      <td>-0.437140</td>\n","      <td>-0.49720</td>\n","      <td>-0.48841</td>\n","      <td>-0.52678</td>\n","      <td>0.087689</td>\n","      <td>-0.38840</td>\n","      <td>-0.54175</td>\n","      <td>1.04710</td>\n","      <td>0.45506</td>\n","      <td>-0.89729</td>\n","      <td>-0.15436</td>\n","      <td>1.92410</td>\n","      <td>-0.61460</td>\n","      <td>-1.51040</td>\n","      <td>1.097900</td>\n","      <td>-1.67890</td>\n","      <td>0.49134</td>\n","      <td>-1.80940</td>\n","      <td>-0.17079</td>\n","      <td>1.2675</td>\n","      <td>-0.79341</td>\n","      <td>1.20490</td>\n","      <td>1.19550</td>\n","      <td>0.04088</td>\n","      <td>1.4088</td>\n","      <td>-0.005838</td>\n","      <td>-1.02170</td>\n","      <td>-1.102600</td>\n","      <td>1.796200</td>\n","      <td>-0.99471</td>\n","      <td>-0.82765</td>\n","      <td>...</td>\n","      <td>0.704110</td>\n","      <td>-0.89341</td>\n","      <td>-1.241900</td>\n","      <td>1.09320</td>\n","      <td>-0.27525</td>\n","      <td>-0.95098</td>\n","      <td>0.62482</td>\n","      <td>-0.93982</td>\n","      <td>0.419370</td>\n","      <td>0.31182</td>\n","      <td>0.774010</td>\n","      <td>-0.12618</td>\n","      <td>-0.32479</td>\n","      <td>-1.53730</td>\n","      <td>0.294840</td>\n","      <td>-0.07272</td>\n","      <td>0.19520</td>\n","      <td>-0.137960</td>\n","      <td>-0.37027</td>\n","      <td>-0.65427</td>\n","      <td>-0.59667</td>\n","      <td>-0.66475</td>\n","      <td>1.05390</td>\n","      <td>-0.024005</td>\n","      <td>-0.96742</td>\n","      <td>-0.10398</td>\n","      <td>0.99047</td>\n","      <td>-0.64226</td>\n","      <td>-0.89341</td>\n","      <td>-0.82218</td>\n","      <td>-1.06990</td>\n","      <td>0.13273</td>\n","      <td>-2.156400</td>\n","      <td>-1.82970</td>\n","      <td>1.28210</td>\n","      <td>-0.75662</td>\n","      <td>1.95380</td>\n","      <td>1.2266</td>\n","      <td>0.111810</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5 rows × 201 columns</p>\n","</div>"],"text/plain":["        0        1         2         3    ...      197     198       199  200\n","0  0.729270 -0.61931 -0.681020 -1.114600  ... -0.39821  1.1243  0.631140    0\n","1 -2.060600  0.81312 -0.127490  1.684300  ...  1.04040  0.4293 -0.097461    0\n","2  0.620090  1.31690 -0.324790 -0.385600  ...  1.53730  1.4301 -0.734350    0\n","3  0.498670 -0.38281 -0.109200 -0.098113  ...  0.63114  1.1668 -2.419800    0\n","4 -0.081829  1.93660  0.064917 -0.447160  ...  1.95380  1.2266  0.111810    0\n","\n","[5 rows x 201 columns]"]},"metadata":{"tags":[]},"execution_count":17}]},{"cell_type":"markdown","metadata":{"id":"gCjZtXVbuARh","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"Z0jmJ7EUxlGF","colab_type":"code","colab":{}},"source":["y = train[200]\n","X = train.drop(200, axis=1)\n","#### Initialize a RandomForestClassifier object, with 20 trees ('n_estimators'), each of maximum depth ('max_depth') 4, while passing \n","#### 'random_state=0' as an argument for repeatability.  \n","####------> \n","clf = \n","#### Then, call the 'fit' method on the classifier object to\n","#### train it using X and y as above\n","####------>\n"],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xJYbfL_oxlGL","colab_type":"text"},"source":["## Extract test data, and apply classifier"]},{"cell_type":"markdown","metadata":{"id":"unHNvCtruHT8","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"FOWqA-ZlxlGM","colab_type":"code","colab":{}},"source":["X2 = test.drop(200, axis=1)\n","y2 = test[200]\n","#### Calculate predictions on the test set by calling the 'predict' method on the trained RandomForestClassifier \n","####------> \n","y_pred2 = "],"execution_count":0,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"X3HC166PxlGR","colab_type":"text"},"source":["## Generate and plot the confusion matrix, and calculate accuracy"]},{"cell_type":"markdown","metadata":{"id":"pbpicJWhuQ3s","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"colab_type":"code","outputId":"46ca991f-b923-494c-d8b6-caffb3145179","id":"1vx6NmwI1yTn","colab":{"base_uri":"https://localhost:8080/","height":569}},"source":["from sklearn.metrics import confusion_matrix\n","#### Use the 'confusion_matrix' function to calculate the confusion matrix for the ground truth and predicted outputs\n","#### on the test data\n","####------> \n","cm = \n","\n","####Build a data frame to help the plotting below\n","cm_melt = {\"variable\":[],\"variable2\":[],\"value\":[]}\n","for column,value in pd.DataFrame(cm).items():\n","    cm_melt['value']+=value.tolist()\n","    cm_melt['variable2'] += range(len(value))\n","    cm_melt['variable']+=[column]*len(value)\n","cm_melt = pd.DataFrame(cm_melt)\n","\n","print(\"Confusion matrix:\")\n","print(cm)\n","\n","#### Evaluate overall accuracy using the trace of the confusion matrix; the trace is the sum over the main diagonal elements\n","####------>  \n","acc = 0\n","for i in range(len(cm)):\n","  acc =  \n","\n","####Normalize the trace by the total number of individuals in the study to get the accuracy\n","acc = acc / len(train)\n","print(\"Overall accuracy: \" + f\"{acc:.3f}\")\n","\n","(\n","    p9.ggplot(cm_melt)\n","      + p9.geom_tile(p9.aes(x = \"variable\", y = \"variable2\",fill=\"value\"))\n","      + p9.scale_fill_continuous(\"Blues\")\n","      + p9.scales.scale_y_reverse()\n",")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Confusion matrix:\n","[[5 3 4 0]\n"," [1 6 4 2]\n"," [4 3 6 4]\n"," [5 3 1 9]]\n","Overall accuracy: 0.433\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{"tags":[]}},{"output_type":"execute_result","data":{"text/plain":["<ggplot: (8787016886732)>"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"markdown","metadata":{"id":"T8O6CYtgxlGY","colab_type":"text"},"source":["## Find most important genes, and plot per-class correlations\n","\n","Use the attribute 'feature_importances_' of the random forest classifier, and rank the genes by this value.  Use the .corr method to calculate the Pearson correlation of the top 10 genes with each class."]},{"cell_type":"markdown","metadata":{"id":"LMj3JcZOupe5","colab_type":"text"},"source":["**FILL IN BELOW**"]},{"cell_type":"code","metadata":{"id":"DvZAPQ1jhVWk","colab_type":"code","outputId":"01655c8b-8a72-4c8d-9403-c4bd504c9dc4","colab":{"base_uri":"https://localhost:8080/","height":798}},"source":["geneIds = pd.read_csv('/content/gdrive/My Drive/[YCMI_CBDS Summer Course] Data/bioinformatics/GeneNames_PEC_filtered.txt', header=None, names=['genes'])\n","\n","print(\"Most important genes:\")\n","\n","#### Use the method 'feature_importances_' from the RandomForestClassifier class to \n","#### get the importance of each feature.\n","####Take the absolute value of the feature importances \n","geneIds['importance'] = \n","\n","#### The values need to be ordered such that the genes with largest coefficients are listed first\n","geneIds = geneIds.sort_values(by='importance', ascending=False).head(10)\n","print(geneIds['genes'])\n","\n","#### Calculate the correlation (function 'corr') between the expression of every one of the top 10 genes across individuals with a binary indicator vector\n","#### (y==j) for class j.\n","arr = {\"class\":[], \"corr\":[], \"gene\":[]}\n","for i, data in geneIds.iterrows():\n","    arr[\"gene\"] += [data['genes']]*4\n","    arr[\"class\"] += range(4)\n","    for j in range(4):\n","      arr['corr'].append(X[i].corr(y==j))\n","  \n","# names for the class values\n","class_names = {0:\"control\", 1:\"schizophrenia\", 2:\"bipolar\", 3:\"autism\"}\n","\n","(\n","    p9.ggplot(pd.DataFrame(arr).replace({'class': class_names}))\n","      + p9.geom_bar(p9.aes(x=\"gene\",y=\"corr\", fill=\"gene\"),stat=\"identity\")\n","      + p9.labs(x=\"\", y=\"\")\n","      + p9.facet_wrap(\"class\", nrow=1) \n","      + p9.theme(axis_text_x = p9.element_text(angle = 90, hjust = 1))\n",")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Most important genes:\n","143     ENSG00000121594.7\n","176     ENSG00000105879.7\n","184     ENSG00000079459.8\n","44     ENSG00000165973.13\n","161     ENSG00000197914.2\n","193     ENSG00000187690.2\n","134     ENSG00000128322.6\n","42      ENSG00000181001.1\n","61      ENSG00000111537.4\n","144    ENSG00000065534.14\n","Name: genes, dtype: object\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 640x480 with 4 Axes>"]},"metadata":{"tags":[]}},{"output_type":"execute_result","data":{"text/plain":["<ggplot: (8787016829893)>"]},"metadata":{"tags":[]},"execution_count":25}]}]}