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%matplotlib inline
from sklearn.datasets import load_iris
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import precision_recall_fscore_support
from sklearn.linear_model import LinearRegression
import matplotlib.pylab as plt
import pandas as pd
import numpy as np
from pandas.tools.plotting import scatter_matrix
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import json
from pprint import pprint
df = pd.DataFrame()
with open('/Users/danielkershaw/Downloads/regression-test') as data_file:
for l in data_file:
data = json.loads(l)
t = {"r_a_l_results_1":data["r_a_l_results"][0]
,"r_a_l_results_2":data["r_a_l_results"][1]
,"observation_level":data["observation_level"]
,"combination":data["combination"]}
df = pd.concat((df, pd.DataFrame(t, index=[data["observation_level"]])))
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df.head()
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df.pivot(index='observation_level', columns='combination', values='r_a_l_results_1')
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df.pivot(index='observation_level', columns='combination', values='r_a_l_results_1').plot(logy=True)
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df.pivot(index='observation_level', columns='combination', values='r_a_l_results_2').plot(logy=False)
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