def main(args):
# Read the arguments
df_f = args.filename
pval_file = args.pval_file
sample_file = args.sample_file
col_to_drop = args.col
threshold = float(args.threshold)
workers = args.workers
print "Config:"
print "Input data frame file name:", df_f
print "Output pvalue file", pval_file
print "Output sample file", sample_file
print "Columns to drop", col_to_drop
print "Threshold", threshold
# Read the time series data
norm_df = pd.read_csv(df_f)
# Drop the column if needed. We typically drop the 1st column as it always is 0 by
# default.
if col_to_drop in norm_df.columns:
cols = norm_df.columns.tolist()
if col_to_drop == norm_df.columns[-1]:
time_points = cols[2:]
new_cols = cols[0:2] + time_points[::-1]
norm_df = norm_df[new_cols]
norm_df.drop(col_to_drop, axis=1, inplace=True)
print "Dropped column", col_to_drop
print "Columns of the data frame are", norm_df.columns
cwords = norm_df.word.values
print "Number of words we are analyzing:", len(cwords)
chunksz = np.ceil(len(cwords) / float(workers))
results = parallelize_func(cwords[:], get_pval_word_chunk, chunksz=chunksz, n_jobs=workers, df=norm_df, B=args.B)
pvals, num_samples = zip(*results)
header = ['word'] + list(norm_df.columns[TS_OFFSET:len(pvals[0]) + 1])
pvalue_df = pd.DataFrame().from_records(list(pvals), columns=header)
# Append additonal columns to the final df
pvalue_df_final = pvalue_df.copy(deep=True)
pvalue_df_final['min_pval'], pvalue_df_final['cp'] = zip(*pvalue_df.apply(get_minpval_cp, axis=1))
pvalue_df_final['tpval'], pvalue_df_final['tcp'] = zip(*pvalue_df.apply(get_cp_pval, axis=1, zscore_df=norm_df, threshold=threshold))
pvalue_df_final.drop(norm_df.columns[TS_OFFSET:len(pvals[0]) + 1], axis=1, inplace = True)
# Write the pvalue output.
num_samples_df = pd.DataFrame().from_records(list(num_samples), columns=header)
num_samples_df.to_csv(sample_file, encoding='utf-8')
# Write the sample output
sdf = pvalue_df_final.sort_values(by=['tpval'])
sdf.to_csv(pval_file, encoding='utf-8')
def main(args):
# Read the arguments
df_f = args.filename
pval_file = args.pval_file
sample_file = args.sample_file
col_to_drop = args.col
threshold = float(args.threshold)
workers = args.workers
print "Config:"
print "Input data frame file name:", df_f
print "Output pvalue file", pval_file
print "Output sample file", sample_file
print "Columns to drop", col_to_drop
print "Threshold", threshold
# Read the time series data
norm_df = pd.read_csv(df_f)
# Drop the column if needed. We typically drop the 1st column as it always is 0 by
# default.
if col_to_drop in norm_df.columns:
cols = norm_df.columns.tolist()
if col_to_drop == norm_df.columns[-1]:
time_points = cols[2:]
new_cols = cols[0:2] + time_points[::-1]
norm_df = norm_df[new_cols]
norm_df.drop(col_to_drop, axis=1, inplace=True)
print "Dropped column", col_to_drop
print "Columns of the data frame are", norm_df.columns
cwords = norm_df.word.values
print "Number of words we are analyzing:", len(cwords)
chunksz = np.ceil(len(cwords) / float(workers))
results = parallelize_func(cwords[:], get_pval_word_chunk, chunksz=chunksz, n_jobs=workers, df=norm_df, B=args.B)
pvals, num_samples = zip(*results)
header = ['word'] + list(norm_df.columns[TS_OFFSET:len(pvals[0]) + 1])
pvalue_df = pd.DataFrame().from_records(list(pvals), columns=header)
# Append additonal columns to the final df
pvalue_df_final = pvalue_df.copy(deep=True)
pvalue_df_final['min_pval'], pvalue_df_final['cp'] = zip(*pvalue_df.apply(get_minpval_cp, axis=1))
pvalue_df_final['tpval'], pvalue_df_final['tcp'] = zip(*pvalue_df.apply(get_cp_pval, axis=1, zscore_df=norm_df, threshold=threshold))
pvalue_df_final.drop(norm_df.columns[TS_OFFSET:len(pvals[0]) + 1], axis=1, inplace = True)
# Write the pvalue output.
num_samples_df = pd.DataFrame().from_records(list(num_samples), columns=header)
num_samples_df.to_csv(sample_file, encoding='utf-8')
# Write the sample output
sdf = pvalue_df_final.sort(columns=['tpval'])
sdf.to_csv(pval_file, encoding='utf-8')
def main(args):
# Read the arguments
df_f = args.filename
common_vocab_file = args.vocab_file
pval_file = args.pval_file
col_to_drop = args.col
should_normalize = not args.dont_normalize
n_jobs = int(args.workers)
cp_pval = args.dump_pval
if args.threshold is not None:
threshold = float(args.threshold)
else:
threshold = None
print "CONFIG:"
print "FILENAME:", df_f
print "VOCAB FILE:", common_vocab_file
print "PVAL_FILE:", pval_file
print "COL TO DROP:", col_to_drop
print "NORMALIZE:", should_normalize
print "Threshold", threshold
# Read the time series data
df = pd.read_csv(df_f)
# Restrict only to the common vocabulary.
df = get_filtered_df(df, common_vocab_file)
# Normalize the data frame
if should_normalize:
norm_df = normalize_timeseries(df)
else:
norm_df = df
# Drop a column if needed.
if col_to_drop in norm_df.columns:
cols = df.columns.tolist()
if col_to_drop == norm_df.columns[-1]:
time_points = cols[2:]
new_cols = cols[0:2] + time_points[::-1]
norm_df = norm_df[new_cols]
print norm_df.columns
norm_df.drop(col_to_drop, axis=1, inplace=True)
print "Columns of the time series", norm_df.columns
cwords = norm_df.word.values
print "Number of words we are processing", len(cwords)
chunksz = np.ceil(len(cwords) / float(n_jobs))
if cp_pval:
results = parallelize_func(cwords[:],
get_pval_word_chunk,
chunksz=chunksz,
n_jobs=n_jobs,
df=norm_df,
threshold=threshold)
cps, pvals = zip(*results)
# R returns 1 for a very high stat significance. So we invert it as for
# us low pvalues mean more significance.
pvals = [(1.0 - pval) for pval in pvals]
actual_cps = [get_actual_cp(norm_df, cp) for cp in cps]
results = zip(cwords, actual_cps, pvals)
header = ['word', 'cp', 'pval']
pvalue_df = pd.DataFrame().from_records(results, columns=header)
sdf = pvalue_df.sort(columns=['pval'])
sdf.to_csv(pval_file, encoding='utf-8', index=None)
else:
results = parallelize_func(cwords[:],
get_cp_word_chunk,
chunksz=chunksz,
n_jobs=n_jobs,
df=norm_df)
cps = results
actual_cps = [get_actual_cp(norm_df, cp) for cp in cps]
results = zip(cwords, actual_cps)
header = ['word', 'cp']
pvalue_df = pd.DataFrame().from_records(results, columns=header)
sdf = pvalue_df.sort(columns=['cp'])
sdf.to_csv(pval_file, encoding='utf-8', index=None)
