Esempi in Python per load_dataframe

Esempio n. 1

# DATAFRAME SAVING
print("\n\n--------------------Started dataframe saving for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_cremad_female))
cremad_f_df_obj = confc.DataFrame(database=confv.database_cremad, gender=confv.gender_female, df=data_info_cremad_df_f)
sl.save_dataframe(cremad_f_df_obj)
print("--------------------Finished dataframe saving for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_cremad_female))
'''

# LOAD REQUIRED PICKLE
print(
    "\n\n--------------------Started dataframe loading for adjusted and {gender} isolated dataset: {name}--------------------"
    .format(gender=confv.gender_female, name=confv.dataset_cremad_female))
cremad_f_df_obj = confc.DataFrame(database=confv.database_cremad,
                                  gender=confv.gender_female)
cremad_f_df_obj = sl.load_dataframe(cremad_f_df_obj)
data_info_cremad_df_f = cremad_f_df_obj.df
print(cremad_f_df_obj.database)
print(cremad_f_df_obj.gender)
print(len(data_info_cremad_df_f))
print(data_info_cremad_df_f.head())
print(data_info_cremad_df_f.tail())
print(cremad_f_df_obj.dataset)
print(cremad_f_df_obj.save_path)
print(
    "--------------------Finished dataframe loading for adjusted and {gender} isolated dataset: {name}--------------------"
    .format(gender=confv.gender_female, name=confv.dataset_cremad_female))
'''
# ORIGINAL DATA DISTRIBUTION ANALYSIS SECTION
print("\n\n--------------------Started original data distribution analysis for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_cremad_female))
pc.emotion_distribution_bar_plot(df=data_info_cremad_df_f, title="{database} - {gender} Isolation - No. of Files".format(database=confv.database_cremad, gender=confv.gender_female))

Esempio n. 2

File: main_ravdess_female.py Progetto: Karawalaya/Stress-Detection-Through-Speech-Emotion-Recognition

# DATAFRAME SAVING
print("\n\n--------------------Started dataframe saving for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_ravdess_female))
ravdess_f_df_obj = confc.DataFrame(database=confv.database_ravdess, gender=confv.gender_female, df=data_info_ravdess_df_f)
sl.save_dataframe(ravdess_f_df_obj)
print("--------------------Finished dataframe saving for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_ravdess_female))
'''

# LOAD REQUIRED PICKLE
print(
    "\n\n--------------------Started dataframe loading for adjusted and {gender} isolated dataset: {name}--------------------"
    .format(gender=confv.gender_female, name=confv.dataset_ravdess_female))
ravdess_f_df_obj = confc.DataFrame(database=confv.database_ravdess,
                                   gender=confv.gender_female)
ravdess_f_df_obj = sl.load_dataframe(ravdess_f_df_obj)
data_info_ravdess_df_f = ravdess_f_df_obj.df
print(ravdess_f_df_obj.database)
print(ravdess_f_df_obj.gender)
print(len(data_info_ravdess_df_f))
print(data_info_ravdess_df_f.head())
print(data_info_ravdess_df_f.tail())
print(ravdess_f_df_obj.dataset)
print(ravdess_f_df_obj.save_path)
print(
    "--------------------Finished dataframe loading for adjusted and {gender} isolated dataset: {name}--------------------"
    .format(gender=confv.gender_female, name=confv.dataset_ravdess_female))
'''
# ORIGINAL DATA DISTRIBUTION ANALYSIS SECTION
print("\n\n--------------------Started original data distribution analysis for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_ravdess_female))
pc.emotion_distribution_bar_plot(df=data_info_ravdess_df_f, title="{database} - {gender} Isolation - No. of Files".format(database=confv.database_ravdess, gender=confv.gender_female))

Esempio n. 3

File: main_shemo_female.py Progetto: Karawalaya/Stress-Detection-Through-Speech-Emotion-Recognition

# DATAFRAME SAVING
print("\n\n--------------------Started dataframe saving for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_shemo_female))
shemo_f_df_obj = confc.DataFrame(database=confv.database_shemo, gender=confv.gender_female, df=data_info_shemo_df_f)
sl.save_dataframe(shemo_f_df_obj)
print("--------------------Finished dataframe saving for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_shemo_female))
'''

# LOAD REQUIRED PICKLE
print(
    "\n\n--------------------Started dataframe loading for adjusted and {gender} isolated dataset: {name}--------------------"
    .format(gender=confv.gender_female, name=confv.dataset_shemo_female))
shemo_f_df_obj = confc.DataFrame(database=confv.database_shemo,
                                 gender=confv.gender_female)
shemo_f_df_obj = sl.load_dataframe(shemo_f_df_obj)
data_info_shemo_df_f = shemo_f_df_obj.df
print(shemo_f_df_obj.database)
print(shemo_f_df_obj.gender)
print(len(data_info_shemo_df_f))
print(data_info_shemo_df_f.head())
print(data_info_shemo_df_f.tail())
print(shemo_f_df_obj.dataset)
print(shemo_f_df_obj.save_path)
print(
    "--------------------Finished dataframe loading for adjusted and {gender} isolated dataset: {name}--------------------"
    .format(gender=confv.gender_female, name=confv.dataset_shemo_female))
'''
# ORIGINAL DATA DISTRIBUTION ANALYSIS SECTION
print("\n\n--------------------Started original data distribution analysis for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_female, name=confv.dataset_shemo_female))
pc.emotion_distribution_bar_plot(df=data_info_shemo_df_f, title="{database} - {gender} Isolation - No. of Files".format(database=confv.database_shemo, gender=confv.gender_female))

Esempio n. 4

File: main_emodb_male.py Progetto: Karawalaya/Stress-Detection-Through-Speech-Emotion-Recognition

# DATAFRAME SAVING
print("\n\n--------------------Started dataframe saving for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_male, name=confv.dataset_emodb_male))
emodb_m_df_obj = confc.DataFrame(database=confv.database_emodb, gender=confv.gender_male, df=data_info_emodb_df_m)
sl.save_dataframe(emodb_m_df_obj)
print("--------------------Finished dataframe saving for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_male, name=confv.dataset_emodb_male))
'''

# LOAD REQUIRED PICKLE
print(
    "\n\n--------------------Started dataframe loading for adjusted and {gender} isolated dataset: {name}--------------------"
    .format(gender=confv.gender_male, name=confv.dataset_emodb_male))
emodb_m_df_obj = confc.DataFrame(database=confv.database_emodb,
                                 gender=confv.gender_male)
emodb_m_df_obj = sl.load_dataframe(emodb_m_df_obj)
data_info_emodb_df_m = emodb_m_df_obj.df
print(emodb_m_df_obj.database)
print(emodb_m_df_obj.gender)
print(len(data_info_emodb_df_m))
print(data_info_emodb_df_m.head())
print(data_info_emodb_df_m.tail())
print(emodb_m_df_obj.dataset)
print(emodb_m_df_obj.save_path)
print(
    "--------------------Finished dataframe loading for adjusted and {gender} isolated dataset: {name}--------------------"
    .format(gender=confv.gender_male, name=confv.dataset_emodb_male))
'''
# ORIGINAL DATA DISTRIBUTION ANALYSIS SECTION
print("\n\n--------------------Started original data distribution analysis for adjusted and {gender} isolated dataset: {name}--------------------".format(gender=confv.gender_male, name=confv.dataset_emodb_male))
pc.emotion_distribution_bar_plot(df=data_info_emodb_df_m, title="{database} - {gender} Isolation - No. of Files".format(database=confv.database_emodb, gender=confv.gender_male))