my_importData_0_small.py

import numpy as np
import pandas as pd

input_file = "0_floor.csv"

# comma delimited is the default
df = pd.read_csv(input_file, header = 0)

# for space delimited use:
# df = pd.read_csv(input_file, header = 0, delimiter = " ")

# for tab delimited use:
# df = pd.read_csv(input_file, header = 0, delimiter = "\t")

# put the original column names in a python list
original_headers = list(df.columns.values)

# remove the non-numeric columns
df = df._get_numeric_data()

# put the numeric column names in a python list
numeric_headers = list(df.columns.values)

# create a numpy array with the numeric values for input into scikit-learn
numpy_array = df.as_matrix()

# reverse the order of the columns
#numeric_headers.reverse()
#reverse_df = df[numeric_headers]

# throughput random forest regression
t = numpy_array[0:168, 3]
x = np.linspace(0, 167, 168)
xall = np.linspace(0, 189, 190)
xtest = np.linspace(168, 189, 22)

from sklearn.ensemble import RandomForestRegressor
#tfit = RandomForestRegressor(100).fit(x[:, None], t).predict(x[:, None])
tfit = RandomForestRegressor(100).fit(numpy_array[0:168, 0:2 ], t).predict(numpy_array[168:190, 0:2])

import matplotlib.pyplot as plt
fig, ax = plt.subplots()

#ax.errorbar(x, t, 0.3, fmt='*', label="Training traffic")
ax.plot(xtest, tfit, '-r', label="Predicted traffic")
ax.errorbar(xtest, numpy_array[168:190, 3], fmt='og', label="Test traffic")
#ax.set_ylabel('Throughput (kbits/second)')
#ax.set_xlabel('Time in hours')
#ax.set_title('Taffic Prediction with Random Forest Regression on ground floor')
#ax.legend(loc="upper left")

plt.show()