def get_best_columns(dirname,
filename,
columns_to_compare=[1, 2],
output_filename='comp_most_wins.csv'):
# Lee el csv
df = fh.read_csv_file(dirname, filename)
best_results = {}
for column_index in columns_to_compare:
best_results.update({df.columns[column_index]: 0})
for index, row in df.iterrows():
row_values = [(df.columns[column_index], row[df.columns[column_index]])
for column_index in columns_to_compare]
best_row_score = max(row_values, key=lambda item: item[1])[1]
for res in row_values:
if res[1] == best_row_score:
best_results[res[0]] += 1
if 'filename' in df.columns:
df = df.drop(columns=['filename'])
df.astype('float64')
fh.write_csv_file(dirname, output_filename, df.columns.tolist(),
[best_results])
def get_column_means(dirname, filename, columns=[1, 2]):
# Lee el csv
df = fh.read_csv_file(dirname, filename)
means = {}
for column_index in columns:
means.update({df.columns[column_index]: None})
def plot_csv_file(dirname, filename, columns_to_compare=[1, 2]):
"""Grafica un csv como gráfica de barras.
El eje X será el número de fila.
El eje Y serán los valores de las columnas a comparar.
"""
# Lee el csv
df = fh.read_csv_file(dirname, filename)
# Define el eje X como una lista de enteros igual al número de filas
number_of_rows = df.shape[0]
x_values = list(range(0, number_of_rows))
# Genera una gráfica de barras para cada columna indicada del csv
bar_charts = []
for i, column_index in enumerate(columns_to_compare):
column_name = df.columns[column_index]
y_values = df[column_name]
color = COLORS[i]
bar_charts.append(
go.Bar(x=x_values,
y=y_values,
name=column_name,
marker_color=color))
# Se genera la figura en dónde se renderizarán las gráficas
fig = go.Figure()
# Se agregan las gráficas de barras generadas previamente
for bc in bar_charts:
fig.add_trace(bc)
# Se renderiza el gráfico completo
fig.update_layout(barmode='group', xaxis_tickangle=-45)
fig.show()
def plot_describe(fileanme):
df = fh.read_csv_file('local_results_evaluation', fileanme)
# pdb.set_trace()
df.describe().iloc[1].plot()
df.describe().iloc[1].to_csv
plt.show()
def describe_results(filename, output_filename='comp_all_in_one_metrics.csv'):
df = fh.read_csv_file('local_results_evaluation', filename)
# pdb.set_trace()
fh.write_file('local_results_evaluation', output_filename,
df.describe().to_csv())
def csv_to_latex_table(dirname,
filename,
columns_to_compare=[1, 2],
columns_to_show=[0, 1, 2]):
df = fh.read_csv_file(dirname, filename, dtype="object")
return dataframe_to_latex_table(df, columns_to_compare)