├── .gitattributes ├── .gitignore ├── Module 1 ├── Chapter 2 │ └── B01782_Ch02_Codes.txt ├── Chapter 3 │ └── Chapter 3 codes.txt ├── Chapter 5 │ └── CHapter 5 Codes.txt ├── Chapter 6 │ └── Chapter 6 Codes.txt ├── Chapter 7 │ └── Chapter 7 Codes.txt ├── Read ME.txt └── Software list.pdf ├── Module 2 └── MasteringPredictiveAnalyticswithPython_Code │ ├── B04881_02_code │ ├── Africa_SHP.zip │ ├── Africa_SHP │ │ ├── Africa.dbf │ │ ├── Africa.shp │ │ └── Africa.shx │ ├── Africa_populations.tsv │ ├── B04881_chapter02_code01.ipynb │ ├── B04881_chapter02_code02.ipynb │ ├── B04881_chapter02_code03.ipynb │ ├── car_crashes.csv │ ├── movies.csv │ └── oil.csv │ ├── B04881_03_code │ └── B04881_chapter03_code │ │ └── B04881_chapter03_code │ │ ├── B04881_chapter03_code01.ipynb │ │ ├── B04881_chapter03_code02.ipynb │ │ ├── Papers.csv │ │ ├── kmeans.txt │ │ └── kmeans2.txt │ ├── B04881_04_code │ ├── B04881_chapter04_code01.ipynb │ ├── B04881_chapter04_code02.ipynb │ └── OnlineNewsPopularity.csv │ ├── B04881_07_code │ ├── B04481_chapter_07_code_01.ipynb │ └── MNIST_data │ │ ├── t10k-images-idx3-ubyte.gz │ │ ├── t10k-labels-idx1-ubyte.gz │ │ ├── train-images-idx3-ubyte.gz │ │ └── train-labels-idx1-ubyte.gz │ ├── B04881_08_code │ ├── B04881_chapter_08_code_01.ipynb │ ├── B04881_chapter_08_code_02.ipynb │ ├── LogisticRegression.py │ ├── Titanic.csv │ ├── dataparser.py │ ├── model_server.py │ ├── modelfactory.py │ ├── modelservice.py │ └── parameters.json │ ├── README.txt │ └── SoftwareList.pdf └── Readme.md /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Windows image file caches 2 | Thumbs.db 3 | ehthumbs.db 4 | 5 | # Folder config file 6 | Desktop.ini 7 | 8 | # Recycle Bin used on file shares 9 | $RECYCLE.BIN/ 10 | 11 | # Windows Installer files 12 | *.cab 13 | *.msi 14 | *.msm 15 | *.msp 16 | 17 | # Windows shortcuts 18 | *.lnk 19 | 20 | # ========================= 21 | # Operating System Files 22 | # ========================= 23 | 24 | # OSX 25 | # ========================= 26 | 27 | .DS_Store 28 | .AppleDouble 29 | .LSOverride 30 | 31 | # Thumbnails 32 | ._* 33 | 34 | # Files that might appear in the root of a volume 35 | .DocumentRevisions-V100 36 | .fseventsd 37 | .Spotlight-V100 38 | .TemporaryItems 39 | .Trashes 40 | .VolumeIcon.icns 41 | 42 | # Directories potentially created on remote AFP share 43 | .AppleDB 44 | .AppleDesktop 45 | Network Trash Folder 46 | Temporary Items 47 | .apdisk 48 | -------------------------------------------------------------------------------- /Module 1/Chapter 2/B01782_Ch02_Codes.txt: -------------------------------------------------------------------------------- 1 | 1. # Importing the dataset 2 | import pandas as pd 3 | data = pd.read_csv('E:/Personal/Learning/Datasets/Book/titanic3.csv') 4 | 5 | 6 | 2. # Passing the directory address and filename as variables 7 | import pandas as pd 8 | path = 'E:/Personal/Learning/Datasets/Book' 9 | filename = 'titanic3.csv' 10 | fullpath = path+'/'+filename 11 | data = pd.read_csv(fullpath) 12 | 13 | 3. # Reading a .txt dataset with a comma delimiter 14 | import pandas as pd 15 | data = read_csv('E:/Personal/Learning/Datasets/Book/Customer Churn Model.txt') 16 | 17 | 18 | 4. # Specifying the column names of dataset from a list 19 | import pandas as pd 20 | data = pd.read_csv('E:/Personal/Learning/Datasets/Book/Customer Churn Model.txt') 21 | data.columns.values 22 | data_columns = pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Customer Churn Columns.csv') 23 | data_column_list = data_columns['Column_Names'].tolist() 24 | data=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Customer Churn Model.txt',header=None,names=data_column_list) 25 | data.columns.values 26 | 27 | 28 | 5. # Opening the dataset using Open method 29 | data=open('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Customer Churn Model.txt','r') 30 | cols=data.next().strip().split(',') 31 | no_cols=len(data.next().strip().split(',')) 32 | 33 | counter=0 34 | main_dict={} 35 | for col in cols: 36 | main_dict[col]=[] 37 | 38 | for line in data: 39 | values = line.strip().split(',') 40 | for i in range(len(cols)): 41 | main_dict[cols[i]].append(values[i]) 42 | counter += 1 43 | 44 | print "The dataset has %d rows and %d columns" % (counter,no_cols) 45 | 46 | import pandas as pd 47 | df=pd.DataFrame(main_dict) 48 | print df.head(5) 49 | 50 | 51 | 52 | 6. # Changing delimiter of a dataset 53 | 54 | infile='E:/Personal/Learning/Datasets/Book/Customer Churn Model.txt' 55 | outfile='E:/Personal/Learning/Datasets/Book/Tab Customer Churn Model.txt' 56 | with open(infile) as infile1: 57 | with open(outfile,'w') as outfile1: 58 | for line in infile1: 59 | fields=line.split(',') 60 | outfile1.write('/t'.join(fields)) 61 | 62 | 63 | 7. # Importing data from a URL 64 | 65 | import pandas as pd 66 | medal_data=pd.read_csv('http://winterolympicsmedals.com/medals.csv') 67 | 68 | 69 | import csv 70 | import urllib2 71 | 72 | url='http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data’ 73 | response=urllib2.urlopen(url) 74 | cr=csv.reader(response) 75 | 76 | for rows in cr: 77 | print rows 78 | 79 | 8. # Reading from an .xls or .xlsx file 80 | 81 | import pandas as pd 82 | data=pd.read_excel('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/titanic3.xls','titanic3') 83 | 84 | import pandas as pd 85 | data=pd.read_excel('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/titanic3.xlsx','titanic3') 86 | 87 | 9. # Writing to a csv or an excel file 88 | 89 | df.to_csv(‘E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Customer Churn Model.csv’ 90 | 91 | 92 | df.to_excel(‘E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Customer Churn Model.csv’ 93 | 94 | 95 | 10. # Summary/DImensions/Structure 96 | 97 | data.head() 98 | data.shape 99 | data.columns.values 100 | data.describe() 101 | data.dtypes 102 | 103 | 104 | 11. # Handling missing Values 105 | 106 | pd.isnull(data[‘body’]) 107 | pd.notnull(data['body']) 108 | pd.isnull(data['body']).values.ravel().sum() 109 | pd.nottnull(data['body']).values.ravel().sum() 110 | data.dropna(axis=0,how='all') 111 | data.dropna(axis=0,how='any') 112 | data.fillna(0) 113 | data.fillna(‘missing’) 114 | data[‘body’].fillna(0) 115 | data['body'] 116 | data['age'] 117 | data['age'].fillna(data['age'].mean()) 118 | data['age'].fillna(method='ffill') 119 | data['age'].fillna(method='backfill') 120 | 121 | 122 | 12. # Dummy Variable 123 | 124 | dummy_sex=pd.get_dummies(data['sex'],prefix='sex') 125 | column_name=data.columns.values.tolist() 126 | column_name.remove('sex') 127 | data[column_name].join(dummy_sex) 128 | 129 | 13. # Basic Scatter Plot 130 | 131 | import pandas as pd 132 | data=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Customer Churn Model.txt') 133 | data.plot(kind='scatter',x='Day Mins',y='Day Charge') 134 | plt.show() 135 | 136 | 14. # Scatter Plots in a 2x2 panel 137 | 138 | import pandas as pd 139 | import matplotlib.pyplot as plt 140 | data=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Customer Churn Model.txt') 141 | data=pd.read_csv('C:/python_data/Customer Churn Model.txt') 142 | figure,axs = plt.subplots(2, 2,sharey=True,sharex=True) 143 | data.plot(kind='scatter',x='Day Mins',y='Day Charge',ax=axs[0][0]) 144 | data.plot(kind='scatter',x='Night Mins',y='Night Charge',ax=axs[0][1]) 145 | data.plot(kind='scatter',x='Day Calls',y='Day Charge',ax=axs[1][0]) 146 | data.plot(kind='scatter',x='Night Calls',y='Night Charge',ax=axs[1][1]) 147 | plt.show() 148 | 149 | 15. # Plotting a Histogram 150 | 151 | import matplotlib.pyplot as plt 152 | plt.hist(data['Day Calls'],bins=8) 153 | plt.xlabel('Day Calls Value') 154 | plt.ylabel('Frequency') 155 | plt.title('Frequency of Day Calls') 156 | plt.show() 157 | 158 | 16. # Plotting a Box Plot 159 | 160 | import matplotlib.pyplot as plt 161 | plt.boxplot(data['Day Calls']) 162 | plt.ylabel('Day Calls') 163 | plt.title('Box Plot of Day Calls') 164 | plt.show() 165 | 166 | -------------------------------------------------------------------------------- /Module 1/Chapter 3/Chapter 3 codes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Advanced-Predictive-Analytics/eda343c48e58b613c28a21a35155f213f4b1771a/Module 1/Chapter 3/Chapter 3 codes.txt -------------------------------------------------------------------------------- /Module 1/Chapter 5/CHapter 5 Codes.txt: -------------------------------------------------------------------------------- 1 | # Step 1: defining the likelihood function 2 | def likelihood(y,pi): 3 | import numpy as np 4 | ll=1 5 | ll_in=range(1,len(y)+1) 6 | for i in range(len(y)): 7 | ll_in[i]=np.where(y[i]==1,pi[i],(1-pi[i])) 8 | ll=ll*ll_in[i] 9 | return ll 10 | 11 | # Step 2: calculating probability for each observation 12 | def logitprob(X,beta): 13 | import numpy as np 14 | rows=np.shape(X)[0] 15 | cols=np.shape(X)[1] 16 | pi=range(1,rows+1) 17 | expon=range(1,rows+1) 18 | for i in range(rows): 19 | expon[i]=0 20 | for j in range(cols): 21 | ex=X[i][j]*beta[j] 22 | expon[i]=ex+expon[i] 23 | with np.errstate(divide='ignore', invalid='ignore'): 24 | pi[i]=np.exp(expon[i])/(1+np.exp(expon[i])) 25 | return pi 26 | 27 | # Step 3: Calculate the W diagonal matrix 28 | def findW(pi): 29 | import numpy as np 30 | W=np.zeros(len(pi)*len(pi)).reshape(len(pi),len(pi)) 31 | for i in range(len(pi)): 32 | print i 33 | W[i,i]=pi[i]*(1-pi[i]) 34 | W[i,i].astype(float) 35 | return W 36 | 37 | # Step 4: defining the logistic function 38 | def logistic(X,Y,limit): 39 | import numpy as np 40 | from numpy import linalg 41 | nrow=np.shape(X)[0] 42 | bias=np.ones(nrow).reshape(nrow,1) 43 | X_new=np.append(X,bias,axis=1) 44 | ncol=np.shape(X_new)[1] 45 | beta=np.zeros(ncol).reshape(ncol,1) 46 | root_diff=np.array(range(1,ncol+1)).reshape(ncol,1) 47 | iter_i=10000 48 | while(iter_i>limit): 49 | print iter_i, limit 50 | pi=logitprob(X_new,beta) 51 | print pi 52 | W=findW(pi) 53 | print W 54 | print X_new 55 | print (Y-np.transpose(pi)) 56 | print np.array((linalg.inv(np.matrix(np.transpose(X_new))*np.matrix(W)*np.matrix(X_new)))*(np.transpose(np.matrix(X_new))*np.matrix(Y-np.transpose(pi)).transpose())) 57 | print beta 58 | print type(np.matrix(np.transpose(Y-np.transpose(pi)))) 59 | print np.matrix(Y-np.transpose(pi)).transpose().shape 60 | print np.matrix(np.transpose(X_new)).shape 61 | root_diff=np.array((linalg.inv(np.matrix(np.transpose(X_new))*np.matrix(W)*np.matrix(X_new)))*(np.transpose(np.matrix(X_new))*np.matrix(Y-np.transpose(pi)).transpose())) 62 | beta=beta+root_diff 63 | iter_i=np.sum(root_diff*root_diff) 64 | ll=likelihood(Y,pi) 65 | print beta 66 | print beta.shape 67 | return beta 68 | 69 | # Testing the model 70 | import numpy as np 71 | X=np.array(range(10)).reshape(10,1) 72 | Y=[0,0,0,0,1,0,1,0,1,1] 73 | bias=np.ones(10).reshape(10,1) 74 | X_new=np.append(X,bias,axis=1) 75 | 76 | 77 | # Running logistic Regression using our function 78 | a=logistic(X,Y,0.000000001) 79 | ll=likelihood(Y,logitprob(X,a)) 80 | 81 | Coefficient of X = 0.66 , Intercept = -3.69 82 | 83 | # From stasmodel.api 84 | import statsmodels.api as sm 85 | logit_model=sm.Logit(Y,X_new) 86 | result=logit_model.fit() 87 | print result.summary() 88 | 89 | Coefficient of X = 0.66 , Intercept = -3.69 90 | 91 | 92 | 93 | # Creating the Contingency Table 94 | import pandas as pd 95 | df=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Logistic Regression/Gender Purchase.csv') 96 | df.head() 97 | 98 | contingency_table=pd.crosstab(df['Gender'],df['Purchase']) 99 | contingency_table 100 | 101 | contingency_table.astype('float').div(contingency_table.sum(axis=1),axis=0) 102 | 103 | 104 | # Importing the dataset 105 | import pandas as pd 106 | bank=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/Book Datasets/Logistic Regression/bank.csv',sep=';') 107 | bank.head() 108 | 109 | 110 | # Processing the data 111 | bank['y']=(bank['y']=='yes').astype(int) 112 | 113 | import numpy as np 114 | bank['education']=np.where(bank['education'] =='basic.9y', 'Basic', bank['education']) 115 | bank['education']=np.where(bank['education'] =='basic.6y', 'Basic', bank['education']) 116 | bank['education']=np.where(bank['education'] =='basic.4y', 'Basic', bank['education']) 117 | bank['education']=np.where(bank['education'] =='university.degree', 'University Degree', bank['education']) 118 | bank['education']=np.where(bank['education'] =='professional.course', 'Professional Course', bank['education']) 119 | bank['education']=np.where(bank['education'] =='high.school', 'High School', bank['education']) 120 | bank['education']=np.where(bank['education'] =='illiterate', 'Illiterate', bank['education']) 121 | bank['education']=np.where(bank['education'] =='unknown', 'Unknown', bank['education']) 122 | 123 | # Data Exploration 124 | bank['y'].value_counts() 125 | bank.groupby('y').mean() 126 | bank.groupby('education').mean() 127 | 128 | # Data Visualisation 129 | 130 | %matplotlib inline 131 | pd.crosstab(bank.education,bank.y).plot(kind='bar') 132 | plt.title('Purchase Frequency for Education Level') 133 | plt.xlabel('Education') 134 | plt.ylabel('Frequency of Purchase') 135 | 136 | table=pd.crosstab(bank.marital,bank.y) 137 | table.div(table.sum(1).astype(float), axis=0).plot(kind='bar', stacked=True) 138 | plt.title('Stacked Bar Chart of Marital Status vs Purchase') 139 | plt.xlabel('Marital Status') 140 | plt.ylabel('Proportion of Customers') 141 | 142 | %matplotlib inline 143 | import matplotlib.pyplot as plt 144 | pd.crosstab(bank.day_of_week,bank.y).plot(kind='bar') 145 | plt.title('Purchase Frequency for Day of Week') 146 | plt.xlabel('Day of Week') 147 | plt.ylabel('Frequency of Purchase') 148 | 149 | import matplotlib.pyplot as plt 150 | bank.age.hist() 151 | plt.title('Histogram of Age') 152 | plt.xlabel('Age') 153 | plt.ylabel('Frequency') 154 | 155 | # Creating dummy variables for categorical variables 156 | cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome'] 157 | for var in cat_vars: 158 | cat_list='var'+'_'+var 159 | cat_list = pd.get_dummies(bank[var], prefix=var) 160 | bank1=bank.join(cat_list) 161 | bank=bank1 162 | 163 | cat_vars=['job','marital','education','default','housing','loan','contact','month','day_of_week','poutcome'] 164 | bank_vars=bank.columns.values.tolist() 165 | to_keep=[i for i in bank_vars if i not in cat_vars] 166 | 167 | bank_final=bank[to_keep] 168 | bank_final.columns.values 169 | 170 | bank_final_vars=bank_final.columns.values.tolist() 171 | Y=['y'] 172 | X=[i for i in bank_final_vars if i not in Y ] 173 | 174 | 175 | # Feature Selection 176 | 177 | from sklearn import datasets 178 | from sklearn.feature_selection import RFE 179 | from sklearn.linear_model import LogisticRegression 180 | 181 | model = LogisticRegression() 182 | 183 | rfe = RFE(model, 12) 184 | rfe = rfe.fit(bank_final[X],bank_final[Y] ) 185 | 186 | print(rfe.support_) 187 | print(rfe.ranking_) 188 | 189 | 190 | cols=['previous', 'euribor3m', 'job_entrepreneur', 'job_self-employed', 'poutcome_success', 'poutcome_failure', 'month_oct', 'month_may', 191 | 'month_mar', 'month_jun', 'month_jul', 'month_dec'] 192 | X=bank_final[cols] 193 | Y=bank_final['y'] 194 | 195 | # Implementing the model 196 | 197 | import statsmodels.api as sm 198 | logit_model=sm.Logit(Y,X) 199 | result=logit_model.fit() 200 | print result.summary() 201 | 202 | from sklearn import linear_model 203 | clf = linear_model.LogisticRegression() 204 | clf.fit(X, Y) 205 | clf.score(X,Y) 206 | 207 | import numpy as np 208 | pd.DataFrame(zip(X.columns, np.transpose(clf.coef_))) 209 | 210 | # Model evaluation and validation 211 | 212 | from sklearn.cross_validation import train_test_split 213 | X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0) 214 | 215 | from sklearn import linear_model 216 | from sklearn import metrics 217 | clf1 = linear_model.LogisticRegression() 218 | clf1.fit(X_train, Y_train) 219 | 220 | probs = clf1.predict_proba(X_test) 221 | predicted = clf1.predict(X_test) 222 | 223 | import pandas as pd 224 | import numpy as np 225 | prob=probs[:,1] 226 | prob_df=pd.DataFrame(prob) 227 | prob_df['predict']=np.where(prob_df[0]>=0.10,1,0) 228 | prob_df.head() 229 | 230 | print metrics.accuracy_score(Y_test, predicted) 231 | 232 | # Cross Validation 233 | 234 | from sklearn.cross_validation import cross_val_score 235 | scores = cross_val_score(linear_model.LogisticRegression(), X, Y, scoring='accuracy', cv=8) 236 | print scores 237 | print scores.mean() 238 | 239 | 240 | # ROC Curve 241 | 242 | from sklearn.cross_validation import train_test_split 243 | from sklearn import linear_model 244 | from sklearn import metrics 245 | X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0) 246 | clf1 = linear_model.LogisticRegression() 247 | clf1.fit(X_train, Y_train) 248 | probs = clf1.predict_proba(X_test) 249 | 250 | prob=probs[:,1] 251 | prob_df=pd.DataFrame(prob) 252 | prob_df['predict']=np.where(prob_df[0]>=0.05,1,0) 253 | prob_df['actual']=Y_test 254 | prob_df.head() 255 | 256 | confusion_matrix=pd.crosstab(prob_df['actual'],prob_df['predict']) 257 | confusion_matrix 258 | 259 | import matplotlib.pyplot as plt 260 | %matplotlib inline 261 | Sensitivity=[1,0.95,0.87,0.62,0.67,0.59,0.5,0.41,0] 262 | FPR=[1,0.76,0.62,0.23,0.27,0.17,0.12,0.07,0] 263 | plt.plot(FPR,Sensitivity,marker='o',linestyle='--',color='r') 264 | x=[i*0.01 for i in range(100)] 265 | y=[i*0.01 for i in range(100)] 266 | plt.plot(x,y) 267 | plt.xlabel('(1-Specificity)') 268 | plt.ylabel('Sensitivity') 269 | plt.title('ROC Curve') 270 | 271 | from sklearn import metrics 272 | from ggplot import * 273 | prob = clf1.predict_proba(X_test)[:,1] 274 | fpr, sensitivity, _ = metrics.roc_curve(Y_test, prob) 275 | df = pd.DataFrame(dict(fpr=fpr, sensitivity=sensitivity)) 276 | ggplot(df, aes(x='fpr', y='sensitivity')) +\ 277 | geom_line() +\ 278 | geom_abline(linetype='dashed') 279 | auc = metrics.auc(fpr,sensitivity) 280 | 281 | ggplot(df, aes(x='fpr', ymin=0, ymax='sensitivity')) +\ 282 | geom_area(alpha=0.2) +\ 283 | geom_line(aes(y='sensitivity')) +\ 284 | ggtitle("ROC Curve w/ AUC=%s" % str(auc)) 285 | 286 | 287 | 288 | 289 | 290 | 291 | 292 | 293 | 294 | 295 | -------------------------------------------------------------------------------- /Module 1/Chapter 6/Chapter 6 Codes.txt: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Advanced-Predictive-Analytics/eda343c48e58b613c28a21a35155f213f4b1771a/Module 1/Chapter 6/Chapter 6 Codes.txt -------------------------------------------------------------------------------- /Module 1/Chapter 7/Chapter 7 Codes.txt: -------------------------------------------------------------------------------- 1 | # Importing the iris dataset 2 | 3 | import pandas as pd 4 | data=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/My Work/Chapter 7/iris.csv') 5 | data.head() 6 | 7 | # Splitting the predictor and target variables 8 | 9 | colnames=data.columns.values.tolist() 10 | predictors=colnames[:4] 11 | target=colnames[4] 12 | 13 | # Splitting the dataset into train and test variables 14 | data['is_train'] = np.random.uniform(0, 1, len(df)) <= .75 15 | train, test = data[data['is_train']==True], data[data['is_train']==False] 16 | 17 | # Creating and fitting a Decision Tree 18 | 19 | dt = DecisionTreeClassifier(criterion='entropy',min_samples_split=20, random_state=99) 20 | dt.fit(train[predictors], train[target]) 21 | 22 | # Predicting the values using the decision tree 23 | 24 | preds=dt.predict(test[predictors]) 25 | pd.crosstab(test['Species'],preds,rownames=['Actual'],colnames=['Predictions']) 26 | 27 | # Visualising the Decision Tree 28 | 29 | from sklearn.tree import export_graphviz 30 | with open('E:/Personal/Learning/Predictive Modeling Book/My Work/Chapter 7/dtree2.dot', 'w') as dotfile: 31 | export_graphviz(dt, out_file = dotfile, feature_names = predictors) 32 | dotfile.close() 33 | 34 | from os import system 35 | system("dot -Tpng /E:/Personal/Learning/Predictive Modeling Book/My Work/Chapter 7/dtree2.dot -o /E:/Personal/Learning/Predictive Modeling Book/My Work/Chapter 7/dtree2.png") 36 | 37 | 38 | # Cross Validating and Pruning the Decision Tree 39 | 40 | X=data[predictors] 41 | Y=data[target] 42 | dt1.fit(X,Y) 43 | dt1 = DecisionTreeClassifier(criterion='entropy',max_depth=5, min_samples_split=20, random_state=99) 44 | 45 | from sklearn.cross_validation import KFold 46 | crossvalidation = KFold(n=X.shape[0], n_folds=10, shuffle=True, random_state=1) 47 | from sklearn.cross_validation import cross_val_score 48 | score = np.mean(cross_val_score(dt1, X, Y, scoring='accuracy', cv=crossvalidation, n_jobs=1)) 49 | score 50 | 51 | # Feature importance of the tree 52 | 53 | dt1.feature_importances_ 54 | 55 | # Importing the Boston dataset 56 | 57 | import pandas as pd 58 | data=pd.read_csv('E:/Personal/Learning/Predictive Modeling Book/My Work/Chapter 7/Boston.csv') 59 | data.head() 60 | 61 | # Preparation for fitting a regression tree 62 | 63 | colnames=data.columns.values.tolist() 64 | predictors=colnames[:13] 65 | target=colnames[13] 66 | X=data[predictors] 67 | Y=data[target] 68 | 69 | # Creating and fitting a regression tree 70 | 71 | from sklearn.tree import DecisionTreeRegressor 72 | regression_tree = DecisionTreeRegressor(min_samples_split=30,min_samples_leaf=10,random_state=0) 73 | regression_tree.fit(X,Y) 74 | 75 | # Comparing actual and the predicted values from the Regression Tree 76 | 77 | reg_tree_pred=regression_tree.predict(data[predictors]) 78 | data['pred']=reg_tree_pred 79 | cols=['pred','medv'] 80 | data[cols] 81 | 82 | #Cross Validating the Regression Tree 83 | 84 | from sklearn.cross_validation import KFold 85 | from sklearn.cross_validation import cross_val_score 86 | import numpy as np 87 | crossvalidation = KFold(n=X.shape[0], n_folds=10,shuffle=True, random_state=1) 88 | score = np.mean(cross_val_score(regression_tree, X, Y,scoring='mean_squared_error', cv=crossvalidation,n_jobs=1)) 89 | score 90 | 91 | # Calculating the feature importance 92 | 93 | regression_tree.feature_importances_ 94 | 95 | # Creating and fitting a Random Forest 96 | 97 | from sklearn.ensemble import RandomForestRegressor 98 | rf = RandomForestRegressor(n_jobs=2,oob_score=True,n_estimators=10) 99 | rf.fit(X,Y) 100 | 101 | # Out of Bag prediction 102 | 103 | rf.oob_prediction_ 104 | 105 | # Comparing actual values with Random Forest predictions 106 | 107 | data['rf_pred']=rf.oob_prediction_ 108 | cols=['rf_pred','medv'] 109 | data[cols].head() 110 | 111 | # Calculating the squared mean error 112 | 113 | data['rf_pred']=rf.oob_prediction_ 114 | data['err']=(data['rf_pred']-data['medv'])**2 115 | sum(data['err'])/506 116 | 117 | # Calculating the oob score 118 | 119 | rf.oob_score_ 120 | 121 | 122 | -------------------------------------------------------------------------------- /Module 1/Read ME.txt: -------------------------------------------------------------------------------- 1 | Chapter 1, 4, 8, 9 does have code files. -------------------------------------------------------------------------------- /Module 1/Software list.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Advanced-Predictive-Analytics/eda343c48e58b613c28a21a35155f213f4b1771a/Module 1/Software list.pdf -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_02_code/Africa_SHP.zip: -------------------------------------------------------------------------------- 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Nigeria 177,155,760 2014 3 | Ethiopia 96,633,456 2014 4 | Egypt 86,895,096 2014 5 | Congo, Democratic Republic of the 77,433,744 2014 6 | Tanzania 49,639,136 2014 7 | South Africa 48,375,644 2014 8 | Kenya 45,010,056 2014 9 | Algeria 38,813,720 2014 10 | Uganda 35,918,916 2014 11 | Sudan 35,482,232 2014 12 | Morocco 32,987,206 2014 13 | Ghana 25,758,108 2014 14 | Mozambique 24,692,144 2014 15 | Madagascar 23,201,926 2014 16 | Cameroon 23,130,708 2014 17 | Cote d'Ivoire 22,848,944 2014 18 | Angola 19,088,106 2014 19 | Burkina Faso 18,365,124 2014 20 | Niger 17,466,172 2014 21 | Malawi 17,377,468 2014 22 | Mali 16,455,903 2014 23 | Zambia 14,638,505 2014 24 | Zimbabwe 13,771,721 2014 25 | Senegal 13,635,927 2014 26 | Rwanda 12,337,138 2014 27 | Guinea 11,474,383 2014 28 | Chad 11,412,107 2014 29 | Tunisia 10,937,521 2014 30 | Somalia 10,428,043 2014 31 | Burundi 10,395,931 2014 32 | Benin 10,160,556 2014 33 | Togo 7,351,374 2014 34 | Eritrea 6,380,803 2014 35 | Libya 6,244,174 2014 36 | Sierra Leone 5,743,725 2014 37 | Central African Republic 5,277,959 2014 38 | Congo, Republic of the 4,662,446 2014 39 | Liberia 4,092,310 2014 40 | Mauritania 3,516,806 2014 41 | Namibia 2,198,406 2014 42 | Botswana 2,155,784 2014 43 | Lesotho 1,942,008 2014 44 | Gambia, The 1,925,527 2014 45 | Guinea-Bissau 1,693,398 2014 46 | Gabon 1,672,597 2014 47 | Swaziland 1,419,623 2014 48 | Mauritius 1,331,155 2014 49 | Djibouti 810,179 2014 50 | Comoros 766,865 2014 51 | Equatorial Guinea 722,254 2014 52 | Western Sahara 554,795 2014 53 | Cape Verde 538,535 2014 54 | Sao Tome and Principe 190,428 2014 55 | Seychelles 91,650 2014 -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_02_code/car_crashes.csv: -------------------------------------------------------------------------------- 1 | Year,Car_Crash_Fatalities_US 1900,36 1901,54 1902,79 1903,117 1904,172 1905,252 1906,338 1907,581 1908,751 1909,1174 1910,1599 1911,2043 1912,2968 1913,4079 1914,4468 1915,6779 1916,7766 1917,9630 1918,10390 1919,10896 1920,12155 1921,13253 1922,14859 1923,17870 1924,18400 1925,20771 1926,22194 1927,24470 1928,26557 1929,29592 1930,31204 1931,31963 1932,27979 1933,29746 1934,34240 1935,34494 1936,36126 1937,37819 1938,31083 1939,30895 1940,32914 1941,38142 1942,27007 1943,22727 1944,23165 1945,26785 1946,31874 1947,31193 1948,30775 1949,30246 1950,33186 1951,35309 1952,36088 1953,36190 1954,33890 1955,36688 1956,37965 1957,36932 1958,35331 1959,36223 1960,36399 1961,36285 1962,38980 1963,41723 1964,45645 1965,47089 1966,50894 1967,50724 1968,52725 1969,53543 1970,52627 1971,52542 1972,54589 1973,54052 1974,45196 1975,44525 1976,45523 1977,47878 1978,50331 1979,51093 1980,51091 1981,49301 1982,43945 1983,42589 1984,44257 1985,43825 1986,46087 1987,46390 1988,47087 1989,45582 1990,44599 1991,41508 1992,39250 1993,40150 1994,40716 1995,41817 1996,42065 1997,42013 1998,41501 1999,41717 2000,41945 2001,42196 2002,43005 2003,42884 2004,42836 2005,43510 2006,42708 2007,41259 2008,37423 2009,33883 -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_02_code/oil.csv: -------------------------------------------------------------------------------- 1 | "Year","Oil prices in constant 1997 dollars. 1870-1997" 2 | "1870",58.53 3 | "1871",49.09 4 | "1872",24.68 5 | "1873",16.71 6 | "1874",19.86 7 | "1875",38.23 8 | "1876",36.11 9 | "1877",19.59 10 | "1878",14.91 11 | "1879",15.74 12 | "1880",15.40 13 | "1881",13.06 14 | "1882",19.07 15 | "1883",15.28 16 | "1884",15.82 17 | "1885",12.77 18 | "1886",12.05 19 | "1887",11.69 20 | "1888",13.85 21 | "1889",13.85 22 | "1890",10.07 23 | "1891",9.17 24 | "1892",10.79 25 | "1893",13.44 26 | "1894",21.17 27 | "1895",18.64 28 | "1896",13.21 29 | "1897",15.54 30 | "1898",21.94 31 | "1899",23.11 32 | "1900",18.64 33 | "1901",14.94 34 | "1902",16.90 35 | "1903",15.46 36 | "1904",11.15 37 | "1905",13.13 38 | "1906",12.48 39 | "1907",12.95 40 | "1908",12.59 41 | "1909",10.58 42 | "1910",10.58 43 | "1911",12.39 44 | "1912",15.53 45 | "1913",13.06 46 | "1914",10.22 47 | "1915",16.33 48 | "1916",19.72 49 | "1917",21.31 50 | "1918",18.84 51 | "1919",24.84 52 | "1920",15.67 53 | "1921",15.57 54 | "1922",12.73 55 | "1923",13.56 56 | "1924",15.54 57 | "1925",17.22 58 | "1926",12.14 59 | "1927",11.07 60 | "1928",12.02 61 | "1929",11.55 62 | "1930",6.92 63 | "1931",10.33 64 | "1932",8.38 65 | "1933",12.11 66 | "1934",11.46 67 | "1935",12.75 68 | "1936",13.32 69 | "1937",13.00 70 | "1938",11.90 71 | "1939",11.79 72 | "1940",12.55 73 | "1941",11.84 74 | "1942",11.25 75 | "1943",11.15 76 | "1944",10.99 77 | "1945",11.70 78 | "1946",14.01 79 | "1947",17.51 80 | "1948",17.27 81 | "1949",16.90 82 | "1950",15.79 83 | "1951",15.45 84 | "1952",16.24 85 | "1953",16.71 86 | "1954",16.77 87 | "1955",16.64 88 | "1956",17.80 89 | "1957",16.87 90 | "1958",16.13 91 | "1959",15.76 92 | "1960",15.66 93 | "1961",15.54 94 | "1962",15.30 95 | "1963",15.05 96 | "1964",14.69 97 | "1965",14.39 98 | "1966",14.18 99 | "1967",13.70 100 | "1968",13.66 101 | "1969",13.27 102 | "1970",13.56 103 | "1971",13.14 104 | "1972",14.19 105 | "1973",22.57 106 | "1974",23.09 107 | "1975",23.31 108 | "1976",22.91 109 | "1977",22.36 110 | "1978",43.06 111 | "1979",64.67 112 | "1980",64.68 113 | "1981",56.90 114 | "1982",48.79 115 | "1983",45.21 116 | "1984",41.40 117 | "1985",22.17 118 | "1986",25.52 119 | "1987",20.28 120 | "1988",22.87 121 | "1989",27.63 122 | "1990",22.95 123 | "1991",21.35 124 | "1992",18.38 125 | "1993",17.15 126 | "1994",18.27 127 | "1995",19.40 128 | "1996",20.52 129 | 130 | Oil prices in constant 1997 dollars. 1870-1997 131 | 132 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_03_code/B04881_chapter03_code/B04881_chapter03_code/B04881_chapter03_code02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "## import dependencies\n", 12 | "from pyspark import SparkContext\n", 13 | "from pyspark.streaming import StreamingContext\n", 14 | "from pyspark.mllib.clustering import StreamingKMeans\n", 15 | "from pyspark.mllib.linalg import Vectors\n", 16 | "from pyspark.mllib.regression import LabeledPoint\n", 17 | "import uuid\n", 18 | "import os\n", 19 | "os.environ[\"PYSPARK_PYTHON\"] = \"python3\"" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": null, 25 | "metadata": { 26 | "collapsed": false 27 | }, 28 | "outputs": [], 29 | "source": [ 30 | "## spark Context and Streaming Spark Context\n", 31 | "job_uuid = 'job_'+ str(uuid.uuid4())\n", 32 | "\n", 33 | "sc = SparkContext( 'local', job_uuid)" 34 | ] 35 | }, 36 | { 37 | "cell_type": "code", 38 | "execution_count": null, 39 | "metadata": { 40 | "collapsed": false 41 | }, 42 | "outputs": [], 43 | "source": [ 44 | "sc.stop()" 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": null, 50 | "metadata": { 51 | "collapsed": true 52 | }, 53 | "outputs": [], 54 | "source": [ 55 | "\n", 56 | "# streaming\n", 57 | "ssc = StreamingContext(sc,10)" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": null, 63 | "metadata": { 64 | "collapsed": true 65 | }, 66 | "outputs": [], 67 | "source": [ 68 | "ssc.stop()" 69 | ] 70 | }, 71 | { 72 | "cell_type": "code", 73 | "execution_count": 5, 74 | "metadata": { 75 | "collapsed": true 76 | }, 77 | "outputs": [], 78 | "source": [ 79 | "# helper function to parse the data\n", 80 | "class Parser():\n", 81 | " def __init__(self,type='train',delimiter=',',num_elements=5, job_uuid=''):\n", 82 | " self.type=type\n", 83 | " self.delimiter=delimiter\n", 84 | " self.num_elements=num_elements\n", 85 | " self.job_uuid=job_uuid\n", 86 | " \n", 87 | " def parse(self,l):\n", 88 | " try:\n", 89 | " line = l.split(self.delimiter) \n", 90 | " if self.type=='test':\n", 91 | " category = float(line[0])\n", 92 | " feature_vector = Vectors.dense(line[1:])\n", 93 | " return LabeledPoint(category, feature_vector)\n", 94 | " elif self.type=='train':\n", 95 | " category = -1\n", 96 | " feature_vector = Vectors.dense(line)\n", 97 | " return LabeledPoint(category, feature_vector)\n", 98 | " else:\n", 99 | " # log exceptions\n", 100 | " f = open('/errors_events/{0}.txt'.format(self.job_uuid),'a')\n", 101 | " f.write('Unknown type: {0}'.format(self.type))\n", 102 | " f.close()\n", 103 | " except:\n", 104 | " # log errors\n", 105 | " f = open('/error_events/{0}.txt'.format(self.job_uuid),'a')\n", 106 | " f.write('Error parsing line: {0}'.format)\n", 107 | " f.close() \n" 108 | ] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": null, 113 | "metadata": { 114 | "collapsed": true 115 | }, 116 | "outputs": [], 117 | "source": [ 118 | "num_features = 2\n", 119 | "training_parser = Parser('train',',',num_features,job_uuid)\n", 120 | "\n", 121 | "\n", 122 | "trainingData = ssc.textFileStream(\"/training_data\").\\\n", 123 | " map(lambda x: training_parser.parse(x)).pprint()\n", 124 | "ssc.start()" 125 | ] 126 | }, 127 | { 128 | "cell_type": "code", 129 | "execution_count": 6, 130 | "metadata": { 131 | "collapsed": false 132 | }, 133 | "outputs": [ 134 | { 135 | "name": "stderr", 136 | "output_type": "stream", 137 | "text": [ 138 | "Traceback (most recent call last):\n", 139 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/util.py\", line 62, in call\n", 140 | " r = self.func(t, *rdds)\n", 141 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/dstream.py\", line 159, in \n", 142 | " func = lambda t, rdd: old_func(rdd)\n", 143 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 579, in update\n", 144 | " self._model.update(rdd, self._decayFactor, self._timeUnit)\n", 145 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 494, in update\n", 146 | " data, decayFactor, timeUnit)\n", 147 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 130, in callMLlibFunc\n", 148 | " return callJavaFunc(sc, api, *args)\n", 149 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in callJavaFunc\n", 150 | " args = [_py2java(sc, a) for a in args]\n", 151 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in \n", 152 | " args = [_py2java(sc, a) for a in args]\n", 153 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in _py2java\n", 154 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 155 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in \n", 156 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 157 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 88, in _py2java\n", 158 | " obj = sc._jvm.SerDe.loads(data)\n", 159 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py\", line 538, in __call__\n", 160 | " self.target_id, self.name)\n", 161 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py\", line 300, in get_return_value\n", 162 | " format(target_id, '.', name), value)\n", 163 | "py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.loads.\n", 164 | ": net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)\n", 165 | "\tat net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)\n", 166 | "\tat net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)\n", 167 | "\tat net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)\n", 168 | "\tat net.razorvine.pickle.Unpickler.load(Unpickler.java:85)\n", 169 | "\tat net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)\n", 170 | "\tat org.apache.spark.mllib.api.python.SerDe$.loads(PythonMLLibAPI.scala:1462)\n", 171 | "\tat org.apache.spark.mllib.api.python.SerDe.loads(PythonMLLibAPI.scala)\n", 172 | "\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n", 173 | "\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n", 174 | "\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n", 175 | "\tat java.lang.reflect.Method.invoke(Method.java:497)\n", 176 | "\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)\n", 177 | "\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)\n", 178 | "\tat py4j.Gateway.invoke(Gateway.java:259)\n", 179 | "\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)\n", 180 | "\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n", 181 | "\tat py4j.GatewayConnection.run(GatewayConnection.java:207)\n", 182 | "\tat java.lang.Thread.run(Thread.java:745)\n", 183 | "\n", 184 | "Traceback (most recent call last):\n", 185 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/util.py\", line 62, in call\n", 186 | " r = self.func(t, *rdds)\n", 187 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/dstream.py\", line 159, in \n", 188 | " func = lambda t, rdd: old_func(rdd)\n", 189 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 579, in update\n", 190 | " self._model.update(rdd, self._decayFactor, self._timeUnit)\n", 191 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 494, in update\n", 192 | " data, decayFactor, timeUnit)\n", 193 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 130, in callMLlibFunc\n", 194 | " return callJavaFunc(sc, api, *args)\n", 195 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in callJavaFunc\n", 196 | " args = [_py2java(sc, a) for a in args]\n", 197 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in \n", 198 | " args = [_py2java(sc, a) for a in args]\n", 199 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in _py2java\n", 200 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 201 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in \n", 202 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 203 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 88, in _py2java\n", 204 | " obj = sc._jvm.SerDe.loads(data)\n", 205 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py\", line 538, in __call__\n", 206 | " self.target_id, self.name)\n", 207 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py\", line 300, in get_return_value\n", 208 | " format(target_id, '.', name), value)\n", 209 | "py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.loads.\n", 210 | ": net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)\n", 211 | "\tat net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)\n", 212 | "\tat net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)\n", 213 | "\tat net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)\n", 214 | "\tat net.razorvine.pickle.Unpickler.load(Unpickler.java:85)\n", 215 | "\tat net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)\n", 216 | "\tat org.apache.spark.mllib.api.python.SerDe$.loads(PythonMLLibAPI.scala:1462)\n", 217 | "\tat org.apache.spark.mllib.api.python.SerDe.loads(PythonMLLibAPI.scala)\n", 218 | "\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n", 219 | "\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n", 220 | "\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n", 221 | "\tat java.lang.reflect.Method.invoke(Method.java:497)\n", 222 | "\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)\n", 223 | "\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)\n", 224 | "\tat py4j.Gateway.invoke(Gateway.java:259)\n", 225 | "\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)\n", 226 | "\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n", 227 | "\tat py4j.GatewayConnection.run(GatewayConnection.java:207)\n", 228 | "\tat java.lang.Thread.run(Thread.java:745)\n", 229 | "\n" 230 | ] 231 | }, 232 | { 233 | "name": "stdout", 234 | "output_type": "stream", 235 | "text": [ 236 | "-------------------------------------------\n", 237 | "Time: 2016-03-18 00:49:40\n", 238 | "-------------------------------------------\n", 239 | "\n", 240 | "-------------------------------------------\n", 241 | "Time: 2016-03-18 00:49:50\n", 242 | "-------------------------------------------\n", 243 | "\n" 244 | ] 245 | }, 246 | { 247 | "name": "stderr", 248 | "output_type": "stream", 249 | "text": [ 250 | "Traceback (most recent call last):\n", 251 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/util.py\", line 62, in call\n", 252 | " r = self.func(t, *rdds)\n", 253 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/dstream.py\", line 159, in \n", 254 | " func = lambda t, rdd: old_func(rdd)\n", 255 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 579, in update\n", 256 | " self._model.update(rdd, self._decayFactor, self._timeUnit)\n", 257 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 494, in update\n", 258 | " data, decayFactor, timeUnit)\n", 259 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 130, in callMLlibFunc\n", 260 | " return callJavaFunc(sc, api, *args)\n", 261 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in callJavaFunc\n", 262 | " args = [_py2java(sc, a) for a in args]\n", 263 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in \n", 264 | " args = [_py2java(sc, a) for a in args]\n", 265 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in _py2java\n", 266 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 267 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in \n", 268 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 269 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 88, in _py2java\n", 270 | " obj = sc._jvm.SerDe.loads(data)\n", 271 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py\", line 538, in __call__\n", 272 | " self.target_id, self.name)\n", 273 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py\", line 300, in get_return_value\n", 274 | " format(target_id, '.', name), value)\n", 275 | "py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.loads.\n", 276 | ": net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)\n", 277 | "\tat net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)\n", 278 | "\tat net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)\n", 279 | "\tat net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)\n", 280 | "\tat net.razorvine.pickle.Unpickler.load(Unpickler.java:85)\n", 281 | "\tat net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)\n", 282 | "\tat org.apache.spark.mllib.api.python.SerDe$.loads(PythonMLLibAPI.scala:1462)\n", 283 | "\tat org.apache.spark.mllib.api.python.SerDe.loads(PythonMLLibAPI.scala)\n", 284 | "\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n", 285 | "\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n", 286 | "\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n", 287 | "\tat java.lang.reflect.Method.invoke(Method.java:497)\n", 288 | "\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)\n", 289 | "\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)\n", 290 | "\tat py4j.Gateway.invoke(Gateway.java:259)\n", 291 | "\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)\n", 292 | "\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n", 293 | "\tat py4j.GatewayConnection.run(GatewayConnection.java:207)\n", 294 | "\tat java.lang.Thread.run(Thread.java:745)\n", 295 | "\n", 296 | "Traceback (most recent call last):\n", 297 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/util.py\", line 62, in call\n", 298 | " r = self.func(t, *rdds)\n", 299 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/dstream.py\", line 159, in \n", 300 | " func = lambda t, rdd: old_func(rdd)\n", 301 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 579, in update\n", 302 | " self._model.update(rdd, self._decayFactor, self._timeUnit)\n", 303 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 494, in update\n", 304 | " data, decayFactor, timeUnit)\n", 305 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 130, in callMLlibFunc\n", 306 | " return callJavaFunc(sc, api, *args)\n", 307 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in callJavaFunc\n", 308 | " args = [_py2java(sc, a) for a in args]\n", 309 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in \n", 310 | " args = [_py2java(sc, a) for a in args]\n", 311 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in _py2java\n", 312 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 313 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in \n", 314 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 315 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 88, in _py2java\n", 316 | " obj = sc._jvm.SerDe.loads(data)\n", 317 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py\", line 538, in __call__\n", 318 | " self.target_id, self.name)\n", 319 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py\", line 300, in get_return_value\n", 320 | " format(target_id, '.', name), value)\n", 321 | "py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.loads.\n", 322 | ": net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)\n", 323 | "\tat net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)\n", 324 | "\tat net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)\n", 325 | "\tat net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)\n", 326 | "\tat net.razorvine.pickle.Unpickler.load(Unpickler.java:85)\n", 327 | "\tat net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)\n", 328 | "\tat org.apache.spark.mllib.api.python.SerDe$.loads(PythonMLLibAPI.scala:1462)\n", 329 | "\tat org.apache.spark.mllib.api.python.SerDe.loads(PythonMLLibAPI.scala)\n", 330 | "\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n", 331 | "\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n", 332 | "\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n", 333 | "\tat java.lang.reflect.Method.invoke(Method.java:497)\n", 334 | "\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)\n", 335 | "\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)\n", 336 | "\tat py4j.Gateway.invoke(Gateway.java:259)\n", 337 | "\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)\n", 338 | "\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n", 339 | "\tat py4j.GatewayConnection.run(GatewayConnection.java:207)\n", 340 | "\tat java.lang.Thread.run(Thread.java:745)\n", 341 | "\n" 342 | ] 343 | }, 344 | { 345 | "name": "stdout", 346 | "output_type": "stream", 347 | "text": [ 348 | "-------------------------------------------\n", 349 | "Time: 2016-03-18 00:50:00\n", 350 | "-------------------------------------------\n", 351 | "\n", 352 | "-------------------------------------------\n", 353 | "Time: 2016-03-18 00:50:10\n", 354 | "-------------------------------------------\n", 355 | "(1.0, 1)\n", 356 | "(2.0, 1)\n", 357 | "(2.0, 1)\n", 358 | "(1.0, 1)\n", 359 | "(1.0, 1)\n", 360 | "(2.0, 1)\n", 361 | "(2.0, 1)\n", 362 | "(2.0, 1)\n", 363 | "(2.0, 1)\n", 364 | "(1.0, 1)\n", 365 | "...\n", 366 | "\n" 367 | ] 368 | }, 369 | { 370 | "name": "stderr", 371 | "output_type": "stream", 372 | "text": [ 373 | "Traceback (most recent call last):\n", 374 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/util.py\", line 62, in call\n", 375 | " r = self.func(t, *rdds)\n", 376 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/dstream.py\", line 159, in \n", 377 | " func = lambda t, rdd: old_func(rdd)\n", 378 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 579, in update\n", 379 | " self._model.update(rdd, self._decayFactor, self._timeUnit)\n", 380 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 494, in update\n", 381 | " data, decayFactor, timeUnit)\n", 382 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 130, in callMLlibFunc\n", 383 | " return callJavaFunc(sc, api, *args)\n", 384 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in callJavaFunc\n", 385 | " args = [_py2java(sc, a) for a in args]\n", 386 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in \n", 387 | " args = [_py2java(sc, a) for a in args]\n", 388 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in _py2java\n", 389 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 390 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in \n", 391 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 392 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 88, in _py2java\n", 393 | " obj = sc._jvm.SerDe.loads(data)\n", 394 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py\", line 538, in __call__\n", 395 | " self.target_id, self.name)\n", 396 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py\", line 300, in get_return_value\n", 397 | " format(target_id, '.', name), value)\n", 398 | "py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.loads.\n", 399 | ": net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)\n", 400 | "\tat net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)\n", 401 | "\tat net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)\n", 402 | "\tat net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)\n", 403 | "\tat net.razorvine.pickle.Unpickler.load(Unpickler.java:85)\n", 404 | "\tat net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)\n", 405 | "\tat org.apache.spark.mllib.api.python.SerDe$.loads(PythonMLLibAPI.scala:1462)\n", 406 | "\tat org.apache.spark.mllib.api.python.SerDe.loads(PythonMLLibAPI.scala)\n", 407 | "\tat sun.reflect.GeneratedMethodAccessor5.invoke(Unknown Source)\n", 408 | "\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n", 409 | "\tat java.lang.reflect.Method.invoke(Method.java:497)\n", 410 | "\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)\n", 411 | "\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)\n", 412 | "\tat py4j.Gateway.invoke(Gateway.java:259)\n", 413 | "\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)\n", 414 | "\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n", 415 | "\tat py4j.GatewayConnection.run(GatewayConnection.java:207)\n", 416 | "\tat java.lang.Thread.run(Thread.java:745)\n", 417 | "\n", 418 | "Traceback (most recent call last):\n", 419 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/util.py\", line 62, in call\n", 420 | " r = self.func(t, *rdds)\n", 421 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/dstream.py\", line 159, in \n", 422 | " func = lambda t, rdd: old_func(rdd)\n", 423 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 579, in update\n", 424 | " self._model.update(rdd, self._decayFactor, self._timeUnit)\n", 425 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 494, in update\n", 426 | " data, decayFactor, timeUnit)\n", 427 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 130, in callMLlibFunc\n", 428 | " return callJavaFunc(sc, api, *args)\n", 429 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in callJavaFunc\n", 430 | " args = [_py2java(sc, a) for a in args]\n", 431 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in \n", 432 | " args = [_py2java(sc, a) for a in args]\n", 433 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in _py2java\n", 434 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 435 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in \n", 436 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 437 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 88, in _py2java\n", 438 | " obj = sc._jvm.SerDe.loads(data)\n", 439 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py\", line 538, in __call__\n", 440 | " self.target_id, self.name)\n", 441 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py\", line 300, in get_return_value\n", 442 | " format(target_id, '.', name), value)\n", 443 | "py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.loads.\n", 444 | ": net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)\n", 445 | "\tat net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)\n", 446 | "\tat net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)\n", 447 | "\tat net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)\n", 448 | "\tat net.razorvine.pickle.Unpickler.load(Unpickler.java:85)\n", 449 | "\tat net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)\n", 450 | "\tat org.apache.spark.mllib.api.python.SerDe$.loads(PythonMLLibAPI.scala:1462)\n", 451 | "\tat org.apache.spark.mllib.api.python.SerDe.loads(PythonMLLibAPI.scala)\n", 452 | "\tat sun.reflect.GeneratedMethodAccessor5.invoke(Unknown Source)\n", 453 | "\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n", 454 | "\tat java.lang.reflect.Method.invoke(Method.java:497)\n", 455 | "\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)\n", 456 | "\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)\n", 457 | "\tat py4j.Gateway.invoke(Gateway.java:259)\n", 458 | "\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)\n", 459 | "\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n", 460 | "\tat py4j.GatewayConnection.run(GatewayConnection.java:207)\n", 461 | "\tat java.lang.Thread.run(Thread.java:745)\n", 462 | "\n" 463 | ] 464 | }, 465 | { 466 | "name": "stdout", 467 | "output_type": "stream", 468 | "text": [ 469 | "-------------------------------------------\n", 470 | "Time: 2016-03-18 00:50:20\n", 471 | "-------------------------------------------\n", 472 | "\n", 473 | "-------------------------------------------\n", 474 | "Time: 2016-03-18 00:50:30\n", 475 | "-------------------------------------------\n", 476 | "\n" 477 | ] 478 | }, 479 | { 480 | "name": "stderr", 481 | "output_type": "stream", 482 | "text": [ 483 | "Traceback (most recent call last):\n", 484 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/util.py\", line 62, in call\n", 485 | " r = self.func(t, *rdds)\n", 486 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/dstream.py\", line 159, in \n", 487 | " func = lambda t, rdd: old_func(rdd)\n", 488 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 579, in update\n", 489 | " self._model.update(rdd, self._decayFactor, self._timeUnit)\n", 490 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 494, in update\n", 491 | " data, decayFactor, timeUnit)\n", 492 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 130, in callMLlibFunc\n", 493 | " return callJavaFunc(sc, api, *args)\n", 494 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in callJavaFunc\n", 495 | " args = [_py2java(sc, a) for a in args]\n", 496 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in \n", 497 | " args = [_py2java(sc, a) for a in args]\n", 498 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in _py2java\n", 499 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 500 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in \n", 501 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 502 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 88, in _py2java\n", 503 | " obj = sc._jvm.SerDe.loads(data)\n", 504 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py\", line 538, in __call__\n", 505 | " self.target_id, self.name)\n", 506 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py\", line 300, in get_return_value\n", 507 | " format(target_id, '.', name), value)\n", 508 | "py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.loads.\n", 509 | ": net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)\n", 510 | "\tat net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)\n", 511 | "\tat net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)\n", 512 | "\tat net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)\n", 513 | "\tat net.razorvine.pickle.Unpickler.load(Unpickler.java:85)\n", 514 | "\tat net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)\n", 515 | "\tat org.apache.spark.mllib.api.python.SerDe$.loads(PythonMLLibAPI.scala:1462)\n", 516 | "\tat org.apache.spark.mllib.api.python.SerDe.loads(PythonMLLibAPI.scala)\n", 517 | "\tat sun.reflect.GeneratedMethodAccessor5.invoke(Unknown Source)\n", 518 | "\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n", 519 | "\tat java.lang.reflect.Method.invoke(Method.java:497)\n", 520 | "\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)\n", 521 | "\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)\n", 522 | "\tat py4j.Gateway.invoke(Gateway.java:259)\n", 523 | "\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)\n", 524 | "\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n", 525 | "\tat py4j.GatewayConnection.run(GatewayConnection.java:207)\n", 526 | "\tat java.lang.Thread.run(Thread.java:745)\n", 527 | "\n", 528 | "Traceback (most recent call last):\n", 529 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/util.py\", line 62, in call\n", 530 | " r = self.func(t, *rdds)\n", 531 | " File \"/Applications/spark-1.5.0/python/pyspark/streaming/dstream.py\", line 159, in \n", 532 | " func = lambda t, rdd: old_func(rdd)\n", 533 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 579, in update\n", 534 | " self._model.update(rdd, self._decayFactor, self._timeUnit)\n", 535 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/clustering.py\", line 494, in update\n", 536 | " data, decayFactor, timeUnit)\n", 537 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 130, in callMLlibFunc\n", 538 | " return callJavaFunc(sc, api, *args)\n", 539 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in callJavaFunc\n", 540 | " args = [_py2java(sc, a) for a in args]\n", 541 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 122, in \n", 542 | " args = [_py2java(sc, a) for a in args]\n", 543 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in _py2java\n", 544 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 545 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 81, in \n", 546 | " obj = ListConverter().convert([_py2java(sc, x) for x in obj], sc._gateway._gateway_client)\n", 547 | " File \"/Applications/spark-1.5.0/python/pyspark/mllib/common.py\", line 88, in _py2java\n", 548 | " obj = sc._jvm.SerDe.loads(data)\n", 549 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/java_gateway.py\", line 538, in __call__\n", 550 | " self.target_id, self.name)\n", 551 | " File \"/Applications/spark-1.5.0/python/lib/py4j-0.8.2.1-src.zip/py4j/protocol.py\", line 300, in get_return_value\n", 552 | " format(target_id, '.', name), value)\n", 553 | "py4j.protocol.Py4JJavaError: An error occurred while calling z:org.apache.spark.mllib.api.python.SerDe.loads.\n", 554 | ": net.razorvine.pickle.PickleException: expected zero arguments for construction of ClassDict (for numpy.dtype)\n", 555 | "\tat net.razorvine.pickle.objects.ClassDictConstructor.construct(ClassDictConstructor.java:23)\n", 556 | "\tat net.razorvine.pickle.Unpickler.load_reduce(Unpickler.java:701)\n", 557 | "\tat net.razorvine.pickle.Unpickler.dispatch(Unpickler.java:171)\n", 558 | "\tat net.razorvine.pickle.Unpickler.load(Unpickler.java:85)\n", 559 | "\tat net.razorvine.pickle.Unpickler.loads(Unpickler.java:98)\n", 560 | "\tat org.apache.spark.mllib.api.python.SerDe$.loads(PythonMLLibAPI.scala:1462)\n", 561 | "\tat org.apache.spark.mllib.api.python.SerDe.loads(PythonMLLibAPI.scala)\n", 562 | "\tat sun.reflect.GeneratedMethodAccessor5.invoke(Unknown Source)\n", 563 | "\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n", 564 | "\tat java.lang.reflect.Method.invoke(Method.java:497)\n", 565 | "\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:231)\n", 566 | "\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:379)\n", 567 | "\tat py4j.Gateway.invoke(Gateway.java:259)\n", 568 | "\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:133)\n", 569 | "\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n", 570 | "\tat py4j.GatewayConnection.run(GatewayConnection.java:207)\n", 571 | "\tat java.lang.Thread.run(Thread.java:745)\n", 572 | "\n" 573 | ] 574 | }, 575 | { 576 | "name": "stdout", 577 | "output_type": "stream", 578 | "text": [ 579 | "-------------------------------------------\n", 580 | "Time: 2016-03-18 00:50:40\n", 581 | "-------------------------------------------\n", 582 | "\n", 583 | "-------------------------------------------\n", 584 | "Time: 2016-03-18 00:50:50\n", 585 | "-------------------------------------------\n", 586 | "\n" 587 | ] 588 | } 589 | ], 590 | "source": [ 591 | "num_features = 2\n", 592 | "num_clusters = 3\n", 593 | "\n", 594 | "training_parser = Parser('train',',',num_features,job_uuid)\n", 595 | "test_parser = Parser('test',',',num_features+1,job_uuid)\n", 596 | "\n", 597 | "trainingData = ssc.textFileStream(\"/training_data\").\\\n", 598 | " map(lambda x: training_parser.parse(x)).map(lambda x: x.features).filter(lambda x: x is not None)\n", 599 | "testData = ssc.textFileStream(\"/test_data\").\\\n", 600 | " map(lambda x: test_parser.parse(x)).filter(lambda x: x is not None)\n", 601 | "streaming_clustering = StreamingKMeans(k=num_clusters, decayFactor=1.0).\\\n", 602 | " setRandomCenters(num_features,0,0)\n", 603 | "streaming_clustering.trainOn(trainingData)\n", 604 | "streaming_clustering.predictOnValues(testData.map(lambda x: (x.label, x.features))).\\\n", 605 | " pprint()\n", 606 | "ssc.start() " 607 | ] 608 | }, 609 | { 610 | "cell_type": "code", 611 | "execution_count": null, 612 | "metadata": { 613 | "collapsed": false 614 | }, 615 | "outputs": [], 616 | "source": [ 617 | "ssc.stop()" 618 | ] 619 | }, 620 | { 621 | "cell_type": "code", 622 | "execution_count": null, 623 | "metadata": { 624 | "collapsed": false 625 | }, 626 | "outputs": [], 627 | "source": [ 628 | "streaming_clustering.latestModel().clusterCenters" 629 | ] 630 | }, 631 | { 632 | "cell_type": "code", 633 | "execution_count": null, 634 | "metadata": { 635 | "collapsed": true 636 | }, 637 | "outputs": [], 638 | "source": [ 639 | "import random\n", 640 | "labels = []\n", 641 | "for i in range(1,100):\n", 642 | " class_num=random.randint(1,3)\n", 643 | " labels.append(class_num)" 644 | ] 645 | }, 646 | { 647 | "cell_type": "code", 648 | "execution_count": null, 649 | "metadata": { 650 | "collapsed": true 651 | }, 652 | "outputs": [], 653 | "source": [ 654 | "import numpy as np\n", 655 | "\n", 656 | "dists = {}\n", 657 | "dists[1] = lambda : [np.random.normal(1,1),np.random.normal(1,2)]\n", 658 | "dists[2] = lambda : [np.random.normal(3,1),np.random.normal(7,2)]\n", 659 | "dists[3] = lambda : [np.random.normal(7,1),np.random.normal(-10,1)]\n", 660 | "\n", 661 | "f=open('sample_train.txt','w')\n", 662 | "for i in labels:\n", 663 | " f.write(\",\".join([str(n) for n in dists[i]()])+'\\n')\n", 664 | "f.close()\n", 665 | "\n", 666 | "f=open('sample_test.txt','w')\n", 667 | "for i in labels:\n", 668 | " f.write(\",\".join([str(i)]+[str(n) for n in dists[i]()])+'\\n')\n", 669 | "f.close()" 670 | ] 671 | }, 672 | { 673 | "cell_type": "code", 674 | "execution_count": null, 675 | "metadata": { 676 | "collapsed": false 677 | }, 678 | "outputs": [], 679 | "source": [ 680 | "streaming_clustering.latestModel().centers" 681 | ] 682 | }, 683 | { 684 | "cell_type": "code", 685 | "execution_count": null, 686 | "metadata": { 687 | "collapsed": false 688 | }, 689 | "outputs": [], 690 | "source": [] 691 | } 692 | ], 693 | "metadata": { 694 | "kernelspec": { 695 | "display_name": "Python 3", 696 | "language": "python", 697 | "name": "python3" 698 | }, 699 | "language_info": { 700 | "codemirror_mode": { 701 | "name": "ipython", 702 | "version": 3 703 | }, 704 | "file_extension": ".py", 705 | "mimetype": "text/x-python", 706 | "name": "python", 707 | "nbconvert_exporter": "python", 708 | "pygments_lexer": "ipython3", 709 | "version": "3.5.1" 710 | } 711 | }, 712 | "nbformat": 4, 713 | "nbformat_minor": 0 714 | } 715 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_03_code/B04881_chapter03_code/B04881_chapter03_code/kmeans.txt: -------------------------------------------------------------------------------- 1 | label x_coord y_coord 2 | 0 10.3115072306 14.268936828 3 | 1 8.62864753558 10.5301395463 4 | 2 10.1329927191 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988 -1.03820834669 -2.32816126483 991 | 989 0.567298713887 2.07753687153 992 | 990 2.3541836015 1.85316563432 993 | 991 0.789749378401 -2.17109949889 994 | 992 -1.0496579872 2.19889120552 995 | 993 -1.15327455245 2.57770960542 996 | 994 2.03066752389 -1.27904847662 997 | 995 0.256944378987 -2.42948137286 998 | 996 -2.32709495969 1.73381293368 999 | 997 0.754290943695 2.03206082705 1000 | 998 -2.50450083456 1.53777329377 1001 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_04_code/B04881_chapter04_code02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "%matplotlib inline\n", 12 | "from pyspark import SparkContext\n", 13 | "from pyspark.sql import SQLContext" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "## spark Context \n", 25 | "sc = SparkContext( 'local', 'pyspark')" 26 | ] 27 | }, 28 | { 29 | "cell_type": "code", 30 | "execution_count": 3, 31 | "metadata": { 32 | "collapsed": false 33 | }, 34 | "outputs": [], 35 | "source": [ 36 | "sqlContext = SQLContext(sc)" 37 | ] 38 | }, 39 | { 40 | "cell_type": "code", 41 | "execution_count": 4, 42 | "metadata": { 43 | "collapsed": true 44 | }, 45 | "outputs": [], 46 | "source": [ 47 | "def parse_line(l):\n", 48 | " try:\n", 49 | " return l.split(\",\")\n", 50 | " except:\n", 51 | " print(\"error in processing {0}\".format(l))" 52 | ] 53 | }, 54 | { 55 | "cell_type": "code", 56 | "execution_count": 5, 57 | "metadata": { 58 | "collapsed": false 59 | }, 60 | "outputs": [], 61 | "source": [ 62 | "songs = sc.textFile('/Users/jbabcock/Downloads/YearPredictionMSD.txt').\\\n", 63 | "map(lambda x : parse_line(x)).\\\n", 64 | "toDF()" 65 | ] 66 | }, 67 | { 68 | "cell_type": "code", 69 | "execution_count": 7, 70 | "metadata": { 71 | "collapsed": true 72 | }, 73 | "outputs": [], 74 | "source": [ 75 | "from pyspark.mllib.regression import LabeledPoint\n", 76 | "songs_labeled = songs.map( lambda x: LabeledPoint(x[0],x[1:]) )" 77 | ] 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 8, 82 | "metadata": { 83 | "collapsed": false 84 | }, 85 | "outputs": [], 86 | "source": [ 87 | "## splitting train\n", 88 | "\n", 89 | "songs_train = songs_labeled.zipWithIndex().\\\n", 90 | "filter( lambda x: x[1] < 463715).\\\n", 91 | "map( lambda x: x[0] )" 92 | ] 93 | }, 94 | { 95 | "cell_type": "code", 96 | "execution_count": 9, 97 | "metadata": { 98 | "collapsed": true 99 | }, 100 | "outputs": [], 101 | "source": [ 102 | "## splitting test\n", 103 | "\n", 104 | "songs_test = songs_labeled.zipWithIndex().\\\n", 105 | "filter( lambda x: x[1] >= 463715).\\\n", 106 | "map( lambda x: x[0] )" 107 | ] 108 | }, 109 | { 110 | "cell_type": "code", 111 | "execution_count": 18, 112 | "metadata": { 113 | "collapsed": false 114 | }, 115 | "outputs": [], 116 | "source": [ 117 | "from pyspark.mllib.tree import RandomForest\n", 118 | "rf = RandomForest.trainRegressor(songs_train,{},50,\"auto\",\"variance\",10,32)" 119 | ] 120 | }, 121 | { 122 | "cell_type": "code", 123 | "execution_count": 19, 124 | "metadata": { 125 | "collapsed": true 126 | }, 127 | "outputs": [], 128 | "source": [ 129 | "prediction = rf.predict(songs_test.map(lambda x: x.features))\n", 130 | "predictedObserved = songs_test.map(lambda lp: lp.label).zip(prediction)" 131 | ] 132 | }, 133 | { 134 | "cell_type": "code", 135 | "execution_count": 20, 136 | "metadata": { 137 | "collapsed": false 138 | }, 139 | "outputs": [], 140 | "source": [ 141 | "from pyspark.mllib.evaluation import RegressionMetrics" 142 | ] 143 | }, 144 | { 145 | "cell_type": "code", 146 | "execution_count": 21, 147 | "metadata": { 148 | "collapsed": false 149 | }, 150 | "outputs": [ 151 | { 152 | "data": { 153 | "text/plain": [ 154 | "-3.69830379609341" 155 | ] 156 | }, 157 | "execution_count": 21, 158 | "metadata": {}, 159 | "output_type": "execute_result" 160 | } 161 | ], 162 | "source": [ 163 | "RegressionMetrics(predictedObserved).r2" 164 | ] 165 | }, 166 | { 167 | "cell_type": "code", 168 | "execution_count": 22, 169 | "metadata": { 170 | "collapsed": false 171 | }, 172 | "outputs": [ 173 | { 174 | "data": { 175 | "text/plain": [ 176 | "[(2007.0, 1995.9064460339775),\n", 177 | " (2003.0, 2002.534486616815),\n", 178 | " (2005.0, 1999.4269553417332),\n", 179 | " (2003.0, 2002.2919078641394),\n", 180 | " (2005.0, 2001.720244097928),\n", 181 | " (2007.0, 1997.7955438534639),\n", 182 | " (2003.0, 2000.5908110171194),\n", 183 | " (2003.0, 1999.874075396483),\n", 184 | " (2003.0, 1997.9664817235432),\n", 185 | " (2005.0, 2001.4931432241076)]" 186 | ] 187 | }, 188 | "execution_count": 22, 189 | "metadata": {}, 190 | "output_type": "execute_result" 191 | } 192 | ], 193 | "source": [ 194 | "predictedObserved.take(10)" 195 | ] 196 | }, 197 | { 198 | "cell_type": "code", 199 | "execution_count": null, 200 | "metadata": { 201 | "collapsed": true 202 | }, 203 | "outputs": [], 204 | "source": [] 205 | } 206 | ], 207 | "metadata": { 208 | "kernelspec": { 209 | "display_name": "Python 2", 210 | "language": "python", 211 | "name": "python2" 212 | }, 213 | "language_info": { 214 | "codemirror_mode": { 215 | "name": "ipython", 216 | "version": 2 217 | }, 218 | "file_extension": ".py", 219 | "mimetype": "text/x-python", 220 | "name": "python", 221 | "nbconvert_exporter": "python", 222 | "pygments_lexer": "ipython2", 223 | "version": "2.7.10" 224 | } 225 | }, 226 | "nbformat": 4, 227 | "nbformat_minor": 0 228 | } 229 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_07_code/B04481_chapter_07_code_01.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "%matplotlib inline\n", 12 | "import tensorflow as tf\n", 13 | "import numpy as np" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": false 21 | }, 22 | "outputs": [ 23 | { 24 | "name": "stdout", 25 | "output_type": "stream", 26 | "text": [ 27 | "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", 28 | "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n", 29 | "Extracting MNIST_data/t10k-images-idx3-ubyte.gz\n", 30 | "Extracting MNIST_data/t10k-labels-idx1-ubyte.gz\n" 31 | ] 32 | } 33 | ], 34 | "source": [ 35 | "# we use the mnist digits to demonstrate image classifaction using a convolutional neural network\n", 36 | "# use the supplied utility to load MNIST data\n", 37 | "\n", 38 | "from tensorflow.examples.tutorials.mnist import input_data\n", 39 | "mnist = input_data.read_data_sets('MNIST_data', one_hot=True)" 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 3, 45 | "metadata": { 46 | "collapsed": false 47 | }, 48 | "outputs": [ 49 | { 50 | "data": { 51 | "text/plain": [ 52 | "55000" 53 | ] 54 | }, 55 | "execution_count": 3, 56 | "metadata": {}, 57 | "output_type": "execute_result" 58 | } 59 | ], 60 | "source": [ 61 | "len(mnist.train.images)" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 4, 67 | "metadata": { 68 | "collapsed": false 69 | }, 70 | "outputs": [ 71 | { 72 | "data": { 73 | "text/plain": [ 74 | "10000" 75 | ] 76 | }, 77 | "execution_count": 4, 78 | "metadata": {}, 79 | "output_type": "execute_result" 80 | } 81 | ], 82 | "source": [ 83 | "len(mnist.test.images)" 84 | ] 85 | }, 86 | { 87 | "cell_type": "code", 88 | "execution_count": 6, 89 | "metadata": { 90 | "collapsed": false 91 | }, 92 | "outputs": [ 93 | { 94 | "data": { 95 | "text/plain": [ 96 | "" 97 | ] 98 | }, 99 | "execution_count": 6, 100 | "metadata": {}, 101 | "output_type": "execute_result" 102 | }, 103 | { 104 | "data": { 105 | "image/png": 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106 | "text/plain": [ 107 | "" 108 | ] 109 | }, 110 | "metadata": {}, 111 | "output_type": "display_data" 112 | } 113 | ], 114 | "source": [ 115 | "from skimage import io\n", 116 | "io.imshow(np.reshape(mnist.train.images[0],(28,28)))" 117 | ] 118 | }, 119 | { 120 | "cell_type": "code", 121 | "execution_count": 7, 122 | "metadata": { 123 | "collapsed": false 124 | }, 125 | "outputs": [ 126 | { 127 | "data": { 128 | "text/plain": [ 129 | "array([ 0., 0., 0., 0., 0., 0., 0., 1., 0., 0.])" 130 | ] 131 | }, 132 | "execution_count": 7, 133 | "metadata": {}, 134 | "output_type": "execute_result" 135 | } 136 | ], 137 | "source": [ 138 | "mnist.train.labels[0]" 139 | ] 140 | }, 141 | { 142 | "cell_type": "code", 143 | "execution_count": 35, 144 | "metadata": { 145 | "collapsed": false, 146 | "scrolled": true 147 | }, 148 | "outputs": [], 149 | "source": [ 150 | "def weight_variable(dimensions,stddev):\n", 151 | " return tf.Variable(tf.truncated_normal(dimensions, stddev=stddev))\n", 152 | "\n", 153 | "def bias_variable(dimensions,constant):\n", 154 | " return tf.Variable(tf.constant(constant, shape=dimensions))\n", 155 | "\n", 156 | "def two_dimensional_convolutional_layer(x, W, strides, padding):\n", 157 | " return tf.nn.conv2d(x, W, strides=strides, padding=padding)\n", 158 | "\n", 159 | "def max_pooling(x,strides,ksize,padding):\n", 160 | " return tf.nn.max_pool(x, ksize=ksize,strides=strides, padding=padding)\n", 161 | "\n", 162 | "def generate_network(weight_variables,\\\n", 163 | " bias_variables,\\\n", 164 | " relu_layers,\\\n", 165 | " pooling_layers,\\\n", 166 | " fully_connected_layers,\\\n", 167 | " inputs,\\\n", 168 | " conv_strides,\\\n", 169 | " pool_stries,\\\n", 170 | " ksize,\\\n", 171 | " output_channels,\\\n", 172 | " conv_field_sizes,\\\n", 173 | " conv_field_depths,\\\n", 174 | " sd_weights\\\n", 175 | " ,bias_mean,\\\n", 176 | " padding,\\\n", 177 | " conv_layers,\\\n", 178 | " fc_layers,\\\n", 179 | " fc_shape,\\\n", 180 | " keep_prob,\\\n", 181 | " class_num,\\\n", 182 | " dropouts):\n", 183 | " \n", 184 | " # add convolutional layers\n", 185 | " for k in range(conv_layers):\n", 186 | " weight_variables.append(weight_variable([conv_field_sizes[k], conv_field_sizes[k], conv_field_depths[k],\\\n", 187 | " output_channels[k]],sd_weights))\n", 188 | " bias_variables.append(bias_variable([output_channels[k]],bias_mean))\n", 189 | " relu_layers.append(tf.nn.relu(two_dimensional_convolutional_layer(inputs[k],weight_variables[k],\\\n", 190 | " conv_strides,padding) + bias_variables[k]))\n", 191 | " pooling_layers.append(max_pooling(relu_layers[k],pool_strides,ksize,padding))\n", 192 | " inputs.append(pooling_layers[k])\n", 193 | " \n", 194 | " # finally, add fully connected layers at end with dropout\n", 195 | " for r in range(fc_layers):\n", 196 | " weight_variables.append(weight_variable(fc_shape,sd_weights))\n", 197 | " bias_variables.append(bias_variable([fc_shape[1]],bias_mean))\n", 198 | " pooling_layers.append(tf.reshape(pooling_layers[-1],[-1,fc_shape[0]]))\n", 199 | " fully_connected_layers.append(tf.nn.relu(tf.matmul(pooling_layers[-1], weight_variables[-1]) + bias_variables[-1]))\n", 200 | " dropouts.append(tf.nn.dropout(fully_connected_layers[-1], keep_prob))\n", 201 | " \n", 202 | " # output layer\n", 203 | " weight_variables.append(weight_variable([fc_shape[1],class_num],sd_weights))\n", 204 | " bias_variables.append(bias_variable([class_num],bias_mean))\n", 205 | " return tf.nn.softmax(tf.matmul(dropouts[-1],weight_variables[-1])+bias_variables[-1])" 206 | ] 207 | }, 208 | { 209 | "cell_type": "code", 210 | "execution_count": 53, 211 | "metadata": { 212 | "collapsed": false 213 | }, 214 | "outputs": [], 215 | "source": [ 216 | "X = tf.placeholder(\"float\", shape=[None, 784])\n", 217 | "observed = tf.placeholder(\"float\", shape=[None, 10])\n", 218 | "images = tf.reshape(X, [-1,28,28,1])\n", 219 | "\n", 220 | "# shape variables\n", 221 | "sd_weights = 0.1\n", 222 | "bias_mean = 0.1\n", 223 | "padding = 'SAME'\n", 224 | "conv_strides = [1,1,1,1]\n", 225 | "pool_strides = [1,2,2,1]\n", 226 | "ksize = [1,2,2,1]\n", 227 | "output_channels = [32,64]\n", 228 | "conv_field_sizes = [5,5]\n", 229 | "conv_field_depths = [1,32]\n", 230 | "fc_shape = [7*7*64,1024]\n", 231 | "keep_prob = tf.placeholder(\"float\")\n", 232 | "class_num = 10\n", 233 | "conv_layers = 2\n", 234 | "fc_layers = 1\n", 235 | "\n", 236 | "# layers variables\n", 237 | "weight_variables = []\n", 238 | "bias_variables = []\n", 239 | "relu_layers = []\n", 240 | "pooling_layers = []\n", 241 | "inputs = [images]\n", 242 | "fully_connected_layers = []\n", 243 | "dropouts = []\n", 244 | "\n", 245 | "prediction = generate_network(weight_variables,\\\n", 246 | " bias_variables,\\\n", 247 | " relu_layers,\\\n", 248 | " pooling_layers,\\\n", 249 | " fully_connected_layers,\\\n", 250 | " inputs,\\\n", 251 | " conv_strides,\\\n", 252 | " pool_strides,\\\n", 253 | " ksize,\\\n", 254 | " output_channels,\\\n", 255 | " conv_field_sizes,\\\n", 256 | " conv_field_depths,\\\n", 257 | " sd_weights\\\n", 258 | " ,bias_mean,\\\n", 259 | " padding,\\\n", 260 | " conv_layers,\\\n", 261 | " fc_layers,\\\n", 262 | " fc_shape,\\\n", 263 | " keep_prob,\\\n", 264 | " class_num,\\\n", 265 | " dropouts)" 266 | ] 267 | }, 268 | { 269 | "cell_type": "code", 270 | "execution_count": 54, 271 | "metadata": { 272 | "collapsed": false 273 | }, 274 | "outputs": [ 275 | { 276 | "name": "stdout", 277 | "output_type": "stream", 278 | "text": [ 279 | "step 0, training accuracy 0.04\n", 280 | "step 100, training accuracy 0.06\n", 281 | "step 200, training accuracy 0.1\n", 282 | "step 300, training accuracy 0.16\n", 283 | "step 400, training accuracy 0.16\n", 284 | "step 500, training accuracy 0.1\n", 285 | "step 600, training accuracy 0.1\n", 286 | "step 700, training accuracy 0.12\n", 287 | "step 800, training accuracy 0.16\n", 288 | "step 900, training accuracy 0.06\n", 289 | "step 1000, training accuracy 0.14\n", 290 | "step 1100, training accuracy 0.06\n", 291 | "step 1200, training accuracy 0.14\n", 292 | "step 1300, training accuracy 0.1\n", 293 | "step 1400, training accuracy 0.08\n", 294 | "step 1500, training accuracy 0.18\n", 295 | "step 1600, training accuracy 0.1\n", 296 | "step 1700, training accuracy 0.18\n", 297 | "step 1800, training accuracy 0.06\n", 298 | "step 1900, training accuracy 0.08\n", 299 | "step 2000, training accuracy 0.16\n", 300 | "step 2100, training accuracy 0.24\n", 301 | "step 2200, training accuracy 0.1\n", 302 | "step 2300, training accuracy 0.18\n", 303 | "step 2400, training accuracy 0.12\n", 304 | "step 2500, training accuracy 0.06\n", 305 | "step 2600, training accuracy 0.1\n", 306 | "step 2700, training accuracy 0.08\n", 307 | "step 2800, training accuracy 0.18\n", 308 | "step 2900, training accuracy 0.1\n", 309 | "step 3000, training accuracy 0.04\n", 310 | "step 3100, training accuracy 0.1\n", 311 | "step 3200, training accuracy 0.12\n", 312 | "step 3300, training accuracy 0\n", 313 | "step 3400, training accuracy 0.18\n", 314 | "step 3500, training accuracy 0.12\n", 315 | "step 3600, training accuracy 0.08\n", 316 | "step 3700, training accuracy 0.18\n", 317 | "step 3800, training accuracy 0.1\n", 318 | "step 3900, training accuracy 0.06\n", 319 | "step 4000, training accuracy 0.02\n", 320 | "step 4100, training accuracy 0.14\n", 321 | "step 4200, training accuracy 0.2\n", 322 | "step 4300, training accuracy 0.16\n", 323 | "step 4400, training accuracy 0.06\n", 324 | "step 4500, training accuracy 0.14\n", 325 | "step 4600, training accuracy 0.06\n", 326 | "step 4700, training accuracy 0.12\n", 327 | "step 4800, training accuracy 0.14\n", 328 | "step 4900, training accuracy 0.16\n", 329 | "step 5000, training accuracy 0.06\n", 330 | "step 5100, training accuracy 0.08\n", 331 | "step 5200, training accuracy 0.12\n", 332 | "step 5300, training accuracy 0.18\n", 333 | "step 5400, training accuracy 0.12\n", 334 | "step 5500, training accuracy 0.22\n", 335 | "step 5600, training accuracy 0.1\n", 336 | "step 5700, training accuracy 0.12\n", 337 | "step 5800, training accuracy 0.02\n", 338 | "step 5900, training accuracy 0.12\n", 339 | "step 6000, training accuracy 0.14\n", 340 | "step 6100, training accuracy 0.18\n", 341 | "step 6200, training accuracy 0.18\n", 342 | "step 6300, training accuracy 0.12\n", 343 | "step 6400, training accuracy 0.14\n", 344 | "step 6500, training accuracy 0.02\n", 345 | "step 6600, training accuracy 0.14\n", 346 | "step 6700, training accuracy 0.12\n", 347 | "step 6800, training accuracy 0.22\n", 348 | "step 6900, training accuracy 0.14\n", 349 | "step 7000, training accuracy 0.12\n", 350 | "step 7100, training accuracy 0.12\n", 351 | "step 7200, training accuracy 0.12\n", 352 | "step 7300, training accuracy 0.12\n", 353 | "step 7400, training accuracy 0.06\n", 354 | "step 7500, training accuracy 0.16\n", 355 | "step 7600, training accuracy 0.16\n", 356 | "step 7700, training accuracy 0.04\n", 357 | "step 7800, training accuracy 0.08\n", 358 | "step 7900, training accuracy 0.22\n", 359 | "step 8000, training accuracy 0.06\n", 360 | "step 8100, training accuracy 0.14\n", 361 | "step 8200, training accuracy 0.08\n", 362 | "step 8300, training accuracy 0.14\n", 363 | "step 8400, training accuracy 0.08\n", 364 | "step 8500, training accuracy 0.06\n" 365 | ] 366 | }, 367 | { 368 | "ename": "KeyboardInterrupt", 369 | "evalue": "", 370 | "output_type": "error", 371 | "traceback": [ 372 | "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", 373 | "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", 374 | "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 10\u001b[0m X: batch[0], observed: batch[1], keep_prob: 1.0})\n\u001b[1;32m 11\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"step %d, training accuracy %g\"\u001b[0m\u001b[0;34m%\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_accuracy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mtrain_step\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeed_dict\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobserved\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbatch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_prob\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0.5\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 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m print(\"test accuracy %g\"%accuracy.eval(feed_dict={\n", 375 | "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, feed_dict, session)\u001b[0m\n\u001b[1;32m 1337\u001b[0m \u001b[0mnone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mdefault\u001b[0m \u001b[0msession\u001b[0m \u001b[0mwill\u001b[0m \u001b[0mbe\u001b[0m \u001b[0mused\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1338\u001b[0m \"\"\"\n\u001b[0;32m-> 1339\u001b[0;31m \u001b[0m_run_using_default_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgraph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1340\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1341\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 376 | "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/framework/ops.py\u001b[0m in \u001b[0;36m_run_using_default_session\u001b[0;34m(operation, feed_dict, graph, session)\u001b[0m\n\u001b[1;32m 2959\u001b[0m \u001b[0;34m\"the operation's graph is different from the session's \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2960\u001b[0m \"graph.\")\n\u001b[0;32m-> 2961\u001b[0;31m \u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2962\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2963\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", 377 | "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict)\u001b[0m\n\u001b[1;32m 371\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 372\u001b[0m \u001b[0;31m# Run request and get response.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 373\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_do_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0munique_fetch_targets\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict_string\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 374\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 375\u001b[0m \u001b[0;31m# User may have fetched the same tensor multiple times, but we\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 378 | "\u001b[0;32m/usr/local/lib/python3.5/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, target_list, fetch_list, feed_dict)\u001b[0m\n\u001b[1;32m 431\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 432\u001b[0m return tf_session.TF_Run(self._session, feed_dict, fetch_list,\n\u001b[0;32m--> 433\u001b[0;31m target_list)\n\u001b[0m\u001b[1;32m 434\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 435\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mStatusNotOK\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", 379 | "\u001b[0;31mKeyboardInterrupt\u001b[0m: " 380 | ] 381 | } 382 | ], 383 | "source": [ 384 | "my_session = tf.InteractiveSession()\n", 385 | "squared_error = tf.reduce_sum(tf.pow(tf.reduce_sum(tf.sub(observed,prediction)),[2]))\n", 386 | "train_step = tf.train.GradientDescentOptimizer(0.01).minimize(squared_error)\n", 387 | "correct_prediction = tf.equal(tf.argmax(prediction,1), tf.argmax(observed,1))\n", 388 | "accuracy = tf.reduce_mean(tf.cast(correct_prediction, \"float\"))\n", 389 | "my_session.run(tf.initialize_all_variables())\n", 390 | "\n", 391 | "for i in range(20000):\n", 392 | " batch = mnist.train.next_batch(50)\n", 393 | " if i%1000 == 0:\n", 394 | " train_accuracy = accuracy.eval(feed_dict={\n", 395 | " X: batch[0], observed: batch[1], keep_prob: 1.0})\n", 396 | " print(\"step %d, training accuracy %g\"%(i, train_accuracy))\n", 397 | " train_step.run(feed_dict={X: batch[0], observed: batch[1], keep_prob: 0.5})\n", 398 | "\n", 399 | "print(\"test accuracy %g\"%accuracy.eval(feed_dict={\n", 400 | " X: mnist.test.images, observed: mnist.test.labels, keep_prob: 1.0}))" 401 | ] 402 | }, 403 | { 404 | "cell_type": "code", 405 | "execution_count": null, 406 | "metadata": { 407 | "collapsed": true 408 | }, 409 | "outputs": [], 410 | "source": [] 411 | } 412 | ], 413 | "metadata": { 414 | "kernelspec": { 415 | "display_name": "Python 3", 416 | "language": "python", 417 | "name": "python3" 418 | }, 419 | "language_info": { 420 | "codemirror_mode": { 421 | "name": "ipython", 422 | "version": 3 423 | }, 424 | "file_extension": ".py", 425 | "mimetype": "text/x-python", 426 | "name": "python", 427 | "nbconvert_exporter": "python", 428 | "pygments_lexer": "ipython3", 429 | "version": "3.5.1" 430 | } 431 | }, 432 | "nbformat": 4, 433 | "nbformat_minor": 0 434 | } 435 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_07_code/MNIST_data/t10k-images-idx3-ubyte.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Advanced-Predictive-Analytics/eda343c48e58b613c28a21a35155f213f4b1771a/Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_07_code/MNIST_data/t10k-images-idx3-ubyte.gz -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_07_code/MNIST_data/t10k-labels-idx1-ubyte.gz: 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2/MasteringPredictiveAnalyticswithPython_Code/B04881_07_code/MNIST_data/train-labels-idx1-ubyte.gz: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Advanced-Predictive-Analytics/eda343c48e58b613c28a21a35155f213f4b1771a/Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_07_code/MNIST_data/train-labels-idx1-ubyte.gz -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_08_code/B04881_chapter_08_code_01.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 8, 6 | "metadata": { 7 | "collapsed": false, 8 | "scrolled": true 9 | }, 10 | "outputs": [ 11 | { 12 | "name": "stderr", 13 | "output_type": "stream", 14 | "text": [ 15 | "[09/Feb/2016:01:09:26] ENGINE Bus STARTING\n", 16 | "INFO:cherrypy.error:[09/Feb/2016:01:09:26] ENGINE Bus STARTING\n", 17 | "[09/Feb/2016:01:09:26] ENGINE Started monitor thread '_TimeoutMonitor'.\n", 18 | "INFO:cherrypy.error:[09/Feb/2016:01:09:26] ENGINE Started monitor thread '_TimeoutMonitor'.\n", 19 | "[09/Feb/2016:01:09:26] ENGINE Started monitor thread 'Autoreloader'.\n", 20 | "INFO:cherrypy.error:[09/Feb/2016:01:09:26] ENGINE Started monitor thread 'Autoreloader'.\n", 21 | "[09/Feb/2016:01:09:26] ENGINE Serving on http://0.0.0.0:5432\n", 22 | "INFO:cherrypy.error:[09/Feb/2016:01:09:26] ENGINE Serving on http://0.0.0.0:5432\n", 23 | "[09/Feb/2016:01:09:26] ENGINE Bus STARTED\n", 24 | "INFO:cherrypy.error:[09/Feb/2016:01:09:26] ENGINE Bus STARTED\n", 25 | "[09/Feb/2016:01:13:38] ENGINE Keyboard Interrupt: shutting down bus\n", 26 | "INFO:cherrypy.error:[09/Feb/2016:01:13:38] ENGINE Keyboard Interrupt: shutting down bus\n", 27 | "[09/Feb/2016:01:13:38] ENGINE Bus STOPPING\n", 28 | "INFO:cherrypy.error:[09/Feb/2016:01:13:38] ENGINE Bus STOPPING\n", 29 | "[09/Feb/2016:01:13:38] ENGINE HTTP Server cherrypy._cpwsgi_server.CPWSGIServer(('0.0.0.0', 5432)) shut down\n", 30 | "INFO:cherrypy.error:[09/Feb/2016:01:13:38] ENGINE HTTP Server cherrypy._cpwsgi_server.CPWSGIServer(('0.0.0.0', 5432)) shut down\n", 31 | "[09/Feb/2016:01:13:38] ENGINE Stopped thread 'Autoreloader'.\n", 32 | "INFO:cherrypy.error:[09/Feb/2016:01:13:38] ENGINE Stopped thread 'Autoreloader'.\n", 33 | "[09/Feb/2016:01:13:38] ENGINE Stopped thread '_TimeoutMonitor'.\n", 34 | "INFO:cherrypy.error:[09/Feb/2016:01:13:38] ENGINE Stopped thread '_TimeoutMonitor'.\n", 35 | "[09/Feb/2016:01:13:38] ENGINE Bus STOPPED\n", 36 | "INFO:cherrypy.error:[09/Feb/2016:01:13:38] ENGINE Bus STOPPED\n", 37 | "[09/Feb/2016:01:13:38] ENGINE Bus EXITING\n", 38 | "INFO:cherrypy.error:[09/Feb/2016:01:13:38] ENGINE Bus EXITING\n", 39 | "[09/Feb/2016:01:13:38] ENGINE Bus EXITED\n", 40 | "INFO:cherrypy.error:[09/Feb/2016:01:13:38] ENGINE Bus EXITED\n", 41 | "[09/Feb/2016:01:13:38] ENGINE Waiting for child threads to terminate...\n", 42 | "INFO:cherrypy.error:[09/Feb/2016:01:13:38] ENGINE Waiting for child threads to terminate...\n", 43 | "[09/Feb/2016:01:13:38] ENGINE Waiting for thread HTTPServer Thread-7.\n", 44 | "INFO:cherrypy.error:[09/Feb/2016:01:13:38] ENGINE Waiting for thread HTTPServer Thread-7.\n" 45 | ] 46 | } 47 | ], 48 | "source": [ 49 | "import cherrypy\n", 50 | "from pyspark import SparkConf, SparkContext\n", 51 | "from modelservice import prediction_service\n", 52 | "\n", 53 | "def start_spark_context():\n", 54 | " conf = SparkConf().setAppName(\"prediction-service\")\n", 55 | " sc = SparkContext(conf=conf, pyFiles=['modelfactory.py', 'modelservice.py'])\n", 56 | "\n", 57 | " return sc\n", 58 | "\n", 59 | "def run_server(app):\n", 60 | "\n", 61 | " cherrypy.config.update({\n", 62 | " 'engine.autoreload.on': True,\n", 63 | " 'log.screen': True,\n", 64 | " 'server.socket_port': 5432,\n", 65 | " 'server.socket_host': '0.0.0.0'\n", 66 | " })\n", 67 | "\n", 68 | " cherrypy.engine.start()\n", 69 | " cherrypy.engine.block()\n", 70 | " \n", 71 | "if __name__ == \"__main__\":\n", 72 | "\n", 73 | " sc = start_spark_context()\n", 74 | " parameters = json.loads(open('parameters.json').readline())\n", 75 | " service = prediction_service('LogisticRegression',parameters,sc)\n", 76 | "\n", 77 | " run_server(service)" 78 | ] 79 | }, 80 | { 81 | "cell_type": "code", 82 | "execution_count": null, 83 | "metadata": { 84 | "collapsed": true 85 | }, 86 | "outputs": [], 87 | "source": [] 88 | } 89 | ], 90 | "metadata": { 91 | "kernelspec": { 92 | "display_name": "Python 3", 93 | "language": "python", 94 | "name": "python3" 95 | }, 96 | "language_info": { 97 | "codemirror_mode": { 98 | "name": "ipython", 99 | "version": 3 100 | }, 101 | "file_extension": ".py", 102 | "mimetype": "text/x-python", 103 | "name": "python", 104 | "nbconvert_exporter": "python", 105 | "pygments_lexer": "ipython3", 106 | "version": "3.5.1" 107 | } 108 | }, 109 | "nbformat": 4, 110 | "nbformat_minor": 0 111 | } 112 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_08_code/B04881_chapter_08_code_02.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 47, 6 | "metadata": { 7 | "collapsed": false 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import requests\n", 12 | "import json" 13 | ] 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 39, 18 | "metadata": { 19 | "collapsed": true 20 | }, 21 | "outputs": [], 22 | "source": [ 23 | "parameters = json.dumps({\"schema\": {\"delimiter\": \",\", \"features\": [{\"values\": {\"1st\": 0, \"2nd\": 1, \"3rd\": 2, \"Crew\": 3}, \"name\": \"Class\", \"pos\": 1}, {\"values\": {\"Female\": 1, \"Male\": 0}, \"name\": \"Sex\", \"pos\": 2}, {\"values\": {\"Child\": 0, \"Adult\": 1}, \"name\": \"Age\", \"pos\": 3}], \"label\": {\"values\": {\"No\": 0.0, \"Yes\": 1.0}, \"pos\": 4}}, \"numFeatures\": 3, \"iterations\": 10, \"numClasses\": 2, \"header\": true})" 24 | ] 25 | }, 26 | { 27 | "cell_type": "code", 28 | "execution_count": 66, 29 | "metadata": { 30 | "collapsed": false 31 | }, 32 | "outputs": [], 33 | "source": [ 34 | "r = requests.get(\"http://0.0.0.0:5000/train/Titanic.csv/{0}\".format(parameters))" 35 | ] 36 | } 37 | ], 38 | "metadata": { 39 | "kernelspec": { 40 | "display_name": "Python 3", 41 | "language": "python", 42 | "name": "python3" 43 | }, 44 | "language_info": { 45 | "codemirror_mode": { 46 | "name": "ipython", 47 | "version": 3 48 | }, 49 | "file_extension": ".py", 50 | "mimetype": "text/x-python", 51 | "name": "python", 52 | "nbconvert_exporter": "python", 53 | "pygments_lexer": "ipython3", 54 | "version": "3.5.1" 55 | } 56 | }, 57 | "nbformat": 4, 58 | "nbformat_minor": 0 59 | } 60 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_08_code/LogisticRegression.py: -------------------------------------------------------------------------------- 1 | from pyspark.mllib.classification import LogisticRegressionWithLBFGS 2 | import json 3 | from dataparser import DataParser 4 | 5 | class LogisticRegression: 6 | 7 | def __init__(self,parameters,sc): 8 | 9 | parameters = json.loads(parameters) 10 | schema = parameters.get('schema',None) 11 | header = parameters.get('header',False) 12 | self._parser = DataParser(schema,header) 13 | self._sc = sc 14 | 15 | def predict(self,input_data): 16 | return self._model.predict(input_data) 17 | 18 | def train(self,input_data,parameters): 19 | iterations = parameters.get('iterations',None) 20 | weights = parameters.get('weights',None) 21 | intercept = parameters.get('intercept',None) 22 | numFeatures = parameters.get('numFeatures',None) 23 | numClasses = parameters.get('numClasses',None) 24 | data = self._sc.parallelize(self._parser.parse(input_data)) 25 | self._model = LogisticRegressionWithLBFGS.train(data,\ 26 | iterations=iterations,\ 27 | numClasses=numClasses) -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_08_code/Titanic.csv: -------------------------------------------------------------------------------- 1 | "","Class","Sex","Age","Survived","Freq" 2 | "1","1st","Male","Child","No",0 3 | "2","2nd","Male","Child","No",0 4 | "3","3rd","Male","Child","No",35 5 | "4","Crew","Male","Child","No",0 6 | "5","1st","Female","Child","No",0 7 | "6","2nd","Female","Child","No",0 8 | "7","3rd","Female","Child","No",17 9 | "8","Crew","Female","Child","No",0 10 | "9","1st","Male","Adult","No",118 11 | "10","2nd","Male","Adult","No",154 12 | "11","3rd","Male","Adult","No",387 13 | "12","Crew","Male","Adult","No",670 14 | "13","1st","Female","Adult","No",4 15 | "14","2nd","Female","Adult","No",13 16 | "15","3rd","Female","Adult","No",89 17 | "16","Crew","Female","Adult","No",3 18 | "17","1st","Male","Child","Yes",5 19 | "18","2nd","Male","Child","Yes",11 20 | "19","3rd","Male","Child","Yes",13 21 | "20","Crew","Male","Child","Yes",0 22 | "21","1st","Female","Child","Yes",1 23 | "22","2nd","Female","Child","Yes",13 24 | "23","3rd","Female","Child","Yes",14 25 | "24","Crew","Female","Child","Yes",0 26 | "25","1st","Male","Adult","Yes",57 27 | "26","2nd","Male","Adult","Yes",14 28 | "27","3rd","Male","Adult","Yes",75 29 | "28","Crew","Male","Adult","Yes",192 30 | "29","1st","Female","Adult","Yes",140 31 | "30","2nd","Female","Adult","Yes",80 32 | "31","3rd","Female","Adult","Yes",76 33 | "32","Crew","Female","Adult","Yes",20 34 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_08_code/dataparser.py: -------------------------------------------------------------------------------- 1 | from pyspark.mllib.regression import LabeledPoint 2 | 3 | class DataParser: 4 | 5 | def __init__(self,schema_dict,header=False): 6 | 7 | ''' 8 | schema takes the form { 9 | label: dict with position, values (also a dict with key -> position mapping) 10 | delimiter: delimiter 11 | features: array of dicts: 12 | dict with position, values (also a dict with key -> position mapping) 13 | } 14 | schema = { 15 | 'delimiter': ',', 16 | 'label': { 'pos': 3, 'values': { 'Yes': 1.0, 'No': 0.0 } }, 17 | 'features': [ 18 | { 'name': 'Class', 'pos': 0, 'values': {'1st':0, '2nd':1, '3rd':2, 'Crew':3 } }, 19 | { 'name': 'Sex', 'pos': 1, 'values': {'Male':0, 'Female':1} }, 20 | { 'name': 'Age', 'pos': 2, 'values': {'Child':0, 'Adult':1} } 21 | ] 22 | } 23 | 24 | 25 | ''' 26 | 27 | 28 | 29 | self.schema_dict = schema_dict 30 | self.header = header 31 | 32 | def parse_line(self,input_line): 33 | 34 | try: 35 | input_array = input_line.strip().replace('\"','').split(self.schema_dict['delimiter']) 36 | 37 | if self.schema_dict['label'].get('values',None) is not None: 38 | label = self.schema_dict['label']['values'][input_array[self.schema_dict['label']['pos']]] 39 | else: 40 | label = input_array[self.schema_dict['label']['pos']] # just get at this position 41 | 42 | features = [] 43 | 44 | for f in self.schema_dict['features']: 45 | # is categorical? 46 | if f.get('values',None) is not None: 47 | cat_feature = [ 0 ] * len(f['values'].keys()) 48 | if input_array[f['pos']]!=len(f['values'].keys()): # 1 hot encoding 49 | cat_feature[f['values'][input_array[f['pos']]]] = 1 50 | features += cat_feature # numerical 51 | else: 52 | features += input_array[f['pos']] 53 | 54 | return LabeledPoint(label,features) 55 | 56 | except: 57 | print('failed to parse line: '.format(input_line)) 58 | pass 59 | 60 | def parse(self,input_data): 61 | 62 | inputs = open(input_data) 63 | 64 | if self.header: 65 | inputs.readline() 66 | 67 | return [ self.parse_line(l) for l in inputs.readlines() ] 68 | 69 | inputs.close() 70 | 71 | 72 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_08_code/model_server.py: -------------------------------------------------------------------------------- 1 | # dependencies 2 | import cherrypy 3 | from pyspark import SparkConf, SparkContext 4 | from modelservice import prediction_service 5 | import json 6 | 7 | def start_spark_context(): 8 | conf = SparkConf().setAppName("prediction-service").set("spark.driver.allowMultipleContexts",True) 9 | sc = SparkContext(conf=conf, pyFiles=['modelfactory.py', 'modelservice.py']) 10 | 11 | return sc 12 | 13 | def run_server(app): 14 | 15 | import paste 16 | from paste.translogger import TransLogger 17 | 18 | app_ = TransLogger(app) 19 | 20 | cherrypy.tree.graft(app_, '/') 21 | 22 | cherrypy.config.update({ 23 | 'engine.autoreload.on': True, 24 | 'log.screen': True, 25 | 'server.socket_port': 5000, 26 | 'server.socket_host': '0.0.0.0' 27 | }) 28 | 29 | cherrypy.engine.start() 30 | cherrypy.engine.block() 31 | 32 | if __name__ == "__main__": 33 | 34 | sc = start_spark_context() 35 | parameters = json.loads(open('parameters.json').readline()) 36 | service = prediction_service('LogisticRegression',parameters,sc) 37 | 38 | run_server(service) -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_08_code/modelfactory.py: -------------------------------------------------------------------------------- 1 | import importlib 2 | 3 | class ModelFactory: 4 | 5 | def train(self,input_data,parameters): 6 | ''' 7 | trains a model based on input data 8 | ''' 9 | self.model.train(input_data,parameters) 10 | 11 | def predict(self,data): 12 | return self.model.predict(data) 13 | 14 | def __init__(self,model_name,model_parameters,sc): 15 | ''' 16 | loads a model class and initializes sparkContext 17 | ''' 18 | module = importlib.import_module(model_name) 19 | model_class = getattr(module, model_name) 20 | self.model = model_class(model_parameters,sc) 21 | 22 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_08_code/modelservice.py: -------------------------------------------------------------------------------- 1 | from flask import Blueprint 2 | main = Blueprint('main', __name__) 3 | 4 | import json 5 | from modelfactory import ModelFactory 6 | 7 | from flask import Flask, request 8 | 9 | import logging 10 | logging.basicConfig(level=logging.INFO) 11 | logger = logging.getLogger(__name__) 12 | 13 | @main.route("/train/",methods=["POST"]) 14 | def train(input_data): 15 | parsed_parameters = request.json 16 | model.train(input_data,parsed_parameters) 17 | return json.dumps("done training") 18 | 19 | @main.route("/predict/") 20 | def predict(input_data): 21 | parsed_data = [float(e) for e in input_data.split(',')] 22 | score = model.predict(parsed_data) 23 | return json.dumps(score) 24 | 25 | def prediction_service(model_name,model_parameters,spark_context): 26 | global model 27 | 28 | model = ModelFactory(model_name,model_parameters,spark_context) 29 | service = Flask(__name__) 30 | service.register_blueprint(main) 31 | return service 32 | -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/B04881_08_code/parameters.json: -------------------------------------------------------------------------------- 1 | "{\"schema\": {\"delimiter\": \",\", \"features\": [{\"values\": {\"1st\": 0, \"2nd\": 1, \"3rd\": 2, \"Crew\": 3}, \"name\": \"Class\", \"pos\": 1}, {\"values\": {\"Female\": 1, \"Male\": 0}, \"name\": \"Sex\", \"pos\": 2}, {\"values\": {\"Child\": 0, \"Adult\": 1}, \"name\": \"Age\", \"pos\": 3}], \"label\": {\"values\": {\"No\": 0.0, \"Yes\": 1.0}, \"pos\": 4}}, \"numFeatures\": 3, \"iterations\": 10, \"numClasses\": 2, \"header\": true}" -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/README.txt: -------------------------------------------------------------------------------- 1 | There are no code files available for chapter 1, 5, 6, and 9. -------------------------------------------------------------------------------- /Module 2/MasteringPredictiveAnalyticswithPython_Code/SoftwareList.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PacktPublishing/Python-Advanced-Predictive-Analytics/eda343c48e58b613c28a21a35155f213f4b1771a/Module 2/MasteringPredictiveAnalyticswithPython_Code/SoftwareList.pdf -------------------------------------------------------------------------------- /Readme.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | ### Download a free PDF 5 | 6 | If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF. 7 |

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