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‎.idea/ML_vs_DL_Bioinformatics_thesis.iml

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‎.idea/libraries/R_User_Library.xml

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‎.idea/misc.xml

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‎.idea/modules.xml

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‎.idea/vcs.xml

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‎.spyproject/codestyle.ini

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[codestyle]
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indentation = True
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[main]
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version = 0.1.0
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‎.spyproject/encoding.ini

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[encoding]
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text_encoding = utf-8
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[main]
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version = 0.1.0
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‎.spyproject/vcs.ini

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[vcs]
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use_version_control = False
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version_control_system =
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[main]
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version = 0.1.0
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‎.spyproject/workspace-DESKTOP-ONUGCSA-2.ini

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[workspace]
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restore_data_on_startup = True
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save_data_on_exit = True
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save_history = True
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save_non_project_files = False
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[main]
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version = 0.1.0
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recent_files = ['C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\40_Richieste_specifiche.py', 'C:\\Users\\anton\\.spyder-py3\\temp.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\15_Modeling.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\10_PCA.py']
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‎.spyproject/workspace-DESKTOP-ONUGCSA-3.ini

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[workspace]
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restore_data_on_startup = True
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save_data_on_exit = True
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save_history = True
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save_non_project_files = False
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[main]
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version = 0.1.0
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recent_files = ['C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\65_Post_processing_NN.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\80_Other_attempt.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\Lasso.py', 'C:\\Users\\anton\\.spyder-py3\\temp.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\20_Modeling_Cancer_type.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\15_Modeling.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\85_Other_attempt.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\17_Modeling_single_cell_line.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\40_Richieste_specifiche.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\Lasso - Copia.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\50_Post_processing.py']
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‎.spyproject/workspace-DESKTOP-ONUGCSA.ini

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[workspace]
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restore_data_on_startup = True
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save_data_on_exit = True
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save_history = True
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save_non_project_files = False
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[main]
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version = 0.1.0
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recent_files = ['C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\15_Modeling.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\01_Importazione_dati_e_moduli.py', 'C:\\Users\\anton\\.spyder-py3\\temp.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\25_Modeling_Deep_Learning.py', 'C:\\Users\\anton\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\Utils.py']
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‎.spyproject/workspace-NAMACALUS220523-2.ini

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[workspace]
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restore_data_on_startup = True
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save_data_on_exit = True
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save_history = True
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save_non_project_files = False
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[main]
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version = 0.1.0
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recent_files = ['C:\\Users\\amacaluso\\.spyder-py3\\temp.py', 'C:\\Users\\amacaluso\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\10_PCA.py', 'C:\\Users\\amacaluso\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\15_Modeling.py', 'C:\\Users\\amacaluso\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\01_Importazione_dati_e_moduli.py']
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‎.spyproject/workspace-NAMACALUS220523-3.ini

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[workspace]
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restore_data_on_startup = True
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save_data_on_exit = True
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save_history = True
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save_non_project_files = False
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[main]
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version = 0.1.0
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recent_files = ['C:\\Users\\amacaluso\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\10_PCA.py']
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‎.spyproject/workspace-NAMACALUS220523.ini

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[workspace]
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restore_data_on_startup = True
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save_data_on_exit = True
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save_history = True
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save_non_project_files = False
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[main]
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version = 0.1.0
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recent_files = ['C:\\Users\\amacaluso\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\Utils.py', 'C:\\Users\\amacaluso\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\25_Modeling_Deep_Learning.py', 'C:\\Users\\amacaluso\\OneDrive - Alma Mater Studiorum Università di Bologna\\Tesi\\Progetto_Tesi_py\\20_Modeling_Cancer_type.py']
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‎.spyproject/workspace.ini

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[workspace]
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restore_data_on_startup = True
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save_data_on_exit = True
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save_history = True
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save_non_project_files = False
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[main]
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version = 0.1.0
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recent_files = ['C:\\Users\\anton\\.spyder-py3\\temp.py']
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‎01_Importazione_dati_e_moduli.py

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# -*- coding: utf-8 -*-
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"""
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Created on Sun Feb 26 17:08:26 2017
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@author: Antonio
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"""
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exec(open("Utils.py").read(), globals())
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###############################################
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############ Importazione dati ################
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###############################################
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data_X = pd.read_csv('dataset/gene_expression.csv', decimal = '.')
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dati_risposta = pd.read_table('dataset/ccle_drug_response.txt', decimal = '.')
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complete_dataframe = pd.merge( dati_risposta, data_X, on = 'ID')
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#print('La dimensione del dataframe completo (variaibli risposta + regressori) e', complete_dataframe.shape)
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X = data_X.drop('ID',1)
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sns.set_style('whitegrid')
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n_rows = X.shape[0]
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n_col = X.shape[1]
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print('La dimensione della matrice dei regressori è ', n_rows, 'x', n_col)
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‎03_Descrittive.py

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# -*- coding: utf-8 -*-
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"""
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Created on Sun Mar 19 11:20:17 2017
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@author: Antonio
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"""
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exec(open("Utils.py").read(), globals())
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exec(open("01_Importazione_dati_e_moduli.py").read(), globals())
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###############################################
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############ Descrittive delle colonne ########
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###############################################
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###############################################
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################## MEDIE ######################
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###############################################
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means = pd.DataFrame( X.apply( np.mean ) )
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summary_means = means.describe(percentiles = [0,1])
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means = X.apply( np.mean )
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sns.kdeplot(np.array( means ), bw=0.5)
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means
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###############################################
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############## Deviazioni standard ############
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###############################################
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std = pd.DataFrame(X.apply( np.std ))
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summary_std = std.describe(percentiles = [0,1])
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std = X.apply( np.std )
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sns.kdeplot(np.array( std ), bw=0.5)
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###############################################
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######### Coefficienti di variazione ##########
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###############################################
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CV = pd.DataFrame(X.apply( stats.variation ))
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summary_cv = CV.describe(percentiles = [0,1])
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CV = X.apply( stats.variation )
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sns.kdeplot(np.array( CV ), bw=0.5)
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###############################################
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###############################################
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################ Analisi missing ##############
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###############################################
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tipo = list(set(dati_risposta.ix[:,2]))
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tipo
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lista_null = []
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for i in tipo:
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dati_correnti = dati_risposta[dati_risposta['Cancer Type'] == i]
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current_n_rows = dati_correnti.shape[0]
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lista_null.append([i,
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current_n_rows,
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dati_correnti.ix[: , 4].isnull().sum(),
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dati_correnti.ix[: , 5].isnull().sum(),
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dati_correnti.ix[: , 6].isnull().sum(),
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dati_correnti.ix[: , 7].isnull().sum()])
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dataframe_null_type = pd.DataFrame.from_records(lista_null)
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dataframe_null_type.columns = ['Cancer_type', 'total_n_row',
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'null_BMS_IC_50','null_BMS_AUC',
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'null_Z_IC_50', 'null_Z_AUC']
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sum_missing = dataframe_null_type.apply( np.sum )
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MIN = X.min().min()
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MAX = X.max().max()
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print(MIN, MAX)
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sns.countplot(x="Cancer Type", data=dati_risposta, palette="Greens_d");
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plt.subplots_adjust(bottom=0.25)
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plt.xticks(rotation=60)
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plt.title('\n Distribuzione variabile Cancer Type')
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savefig("Presentazione/frequenze.png") #, transparent=True)
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plt.show()
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import matplotlib.pyplot as plt
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dati_risposta.ix[:, [2, 4]].boxplot( by='Cancer Type')
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plt.subplots_adjust(bottom=0.15)
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plt.xticks(rotation=60)
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savefig("Presentazione/boxplot1.png", dpi = 900) #, transparent=True) #), dpi = 500)
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plt.show()
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dati_risposta.ix[:, [2, 5]].boxplot( by='Cancer Type')
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plt.subplots_adjust(bottom=0.25)
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plt.xticks(rotation=60)
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savefig("Presentazione/boxplot2.png") #, transparent=True) #), dpi = 500)
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plt.show()
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dati_risposta.ix[:, [2, 6]].boxplot( by='Cancer Type')
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plt.subplots_adjust(bottom=0.25)
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plt.xticks(rotation=60)
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savefig("Presentazione/boxplot3.png") #, transparent=True) #), dpi = 500)
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plt.show()
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dati_risposta.ix[:, [2, 7]].boxplot( by='Cancer Type')
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plt.subplots_adjust(bottom=0.25)
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plt.xticks(rotation=60)
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savefig("Presentazione/boxplot4.png") #, transparent=True) #), dpi = 500)
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plt.show()

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