def synthetic_control(before_interv_data, after_interv_data,
after_interv_dates, before_interv_dates, selected_ixp,
other_ixps):
singvals = 4
trainDF = pd.DataFrame(data=before_interv_data)
testDF = pd.DataFrame(data=after_interv_data[other_ixps])
rscModel = RobustSyntheticControl(selected_ixp,
singvals,
len(trainDF),
probObservation=1.0,
modelType='svd',
svdMethod='numpy',
otherSeriesKeysArray=other_ixps)
rscModel.fit(trainDF)
denoisedDF = rscModel.model.denoisedDF()
print(rscModel.model.weights)
predictions = []
predictions = np.dot(testDF[other_ixps], rscModel.model.weights)
model_fit = np.dot(trainDF[other_ixps][:], rscModel.model.weights)
plt.plot(after_interv_dates,
predictions,
color='red',
label="counterfactual",
linewidth=1)
plt.plot(before_interv_dates,
model_fit,
color='green',
label="fitted model",
linewidth=1)
trainDF = pd.DataFrame(data=trainDataDict)
testDF = pd.DataFrame(data=testDataDict)
# model
rscModel = RobustSyntheticControl(
basqueKey,
singvals,
len(trainDF),
probObservation=1.0,
modelType="svd",
svdMethod="numpy",
otherSeriesKeysArray=otherStates,
)
# fit the model
rscModel.fit(trainDF)
# save the denoised training data
denoisedDF = rscModel.model.denoisedDF()
# predict - all at once
predictions = rscModel.predict(testDF)
# plot
yearsToPlot = range(yearStart, yearTestEnd, 1)
interventionYear = yearTrainEnd - 1
plt.plot(
yearsToPlot,
np.append(trainMasterDF[basqueKey], testDF[basqueKey], axis=0),
color="red",
label="observations",
def runAnalysis(N, T, TrainingEnd, rowRank, colRank):
# generate metric matrices
genFunctionOne = simpleFunctionOne
genFunctionTwo = simpleFunctionTwo
trueWeights = np.random.uniform(0.0, 1.0, N)
trueWeights = trueWeights / np.sum(trueWeights)
thetaArrayParams = np.random.uniform(0.0, 1.0, rowRank)
rhoArrayParams = np.random.uniform(0.0, 1.0, colRank)
rowParams = np.random.choice(thetaArrayParams, N)
colParams = np.random.choice(rhoArrayParams, T)
# metric 1
(observationMatrix1, meanMatrix1, trainDF1, testDF1, meanTrainingDict1,
meanTestDict1) = generateOneMetricMatrix(N, T, TrainingEnd, rowRank,
colRank, genFunctionOne,
trueWeights, rowParams,
colParams)
# metric 2
(observationMatrix2, meanMatrix2, trainDF2, testDF2, meanTrainingDict2,
meanTestDict2) = generateOneMetricMatrix(N, T, TrainingEnd, rowRank,
colRank, genFunctionTwo,
trueWeights, rowParams,
colParams)
keySeriesLabel = '0'
otherSeriesLabels = []
for ind in range(1, N + 1):
otherSeriesLabels.append(str(ind))
# RSC analysis
singvals = 8
############################
#### RSC for metric 1
rscmodel1 = RobustSyntheticControl(keySeriesLabel,
singvals,
len(trainDF1),
probObservation=1.0,
svdMethod='numpy',
otherSeriesKeysArray=otherSeriesLabels)
# fit the model
rscmodel1.fit(trainDF1)
predictionsRSC1 = rscmodel1.predict(testDF1)
rscRMSE1 = np.sqrt(
np.mean((predictionsRSC1 - meanTestDict1[keySeriesLabel])**2))
#print("\n\n *** RSC rmse1:")
#print(rscRMSE1)
############################
##### RSC for metric 2
rscmodel2 = RobustSyntheticControl(keySeriesLabel,
singvals,
len(trainDF2),
probObservation=1.0,
svdMethod='numpy',
otherSeriesKeysArray=otherSeriesLabels)
# fit the model
rscmodel2.fit(trainDF2)
predictionsRSC2 = rscmodel2.predict(testDF2)
rscRMSE2 = np.sqrt(
np.mean((predictionsRSC2 - meanTestDict2[keySeriesLabel])**2))
#print("\n\n *** RSC rmse2:")
#print(rscRMSE2)
############################
#### multi RSC model (combined) --
relative_weights = [1.0, 1.0]
# instantiate the model
mrscmodel = MultiRobustSyntheticControl(
2,
relative_weights,
keySeriesLabel,
singvals,
len(trainDF1),
probObservation=1.0,
svdMethod='numpy',
otherSeriesKeysArray=otherSeriesLabels)
# fit
mrscmodel.fit([trainDF1, trainDF2])
# predict
combinedPredictionsArray = mrscmodel.predict(
[testDF1[otherSeriesLabels], testDF2[otherSeriesLabels]])
# split the predictions for the metrics
predictionsmRSC_1 = combinedPredictionsArray[0]
predictionsmRSC_2 = combinedPredictionsArray[1]
# compute RMSE
mrscRMSE1 = np.sqrt(
np.mean((predictionsmRSC_1 - meanTestDict1[keySeriesLabel])**2))
mrscRMSE2 = np.sqrt(
np.mean((predictionsmRSC_2 - meanTestDict2[keySeriesLabel])**2))
#print("\n\n *** mRSC rmse1:")
#print(mrscRMSE1)
#print("\n\n *** mRSC rmse2:")
#print(mrscRMSE1)
return ({
"rsc1": rscRMSE1,
"rsc2": rscRMSE2,
"mrsc1": mrscRMSE1,
"mrsc2": mrscRMSE2
})
def basque(filename):
# BASQUE COUNTRY STUDY
df = pd.read_csv(filename)
pivot = df.pivot_table(values='gdpcap', index='regionname', columns='year')
pivot = pivot.drop('Spain (Espana)')
dfBasque = pd.DataFrame(pivot.to_records())
allColumns = dfBasque.columns.values
states = list(np.unique(dfBasque['regionname']))
years = np.delete(allColumns, [0])
basqueKey = 'Basque Country (Pais Vasco)'
states.remove(basqueKey)
otherStates = states
yearStart = 1955
yearTrainEnd = 1971
yearTestEnd = 1998
singvals = 1
p = 0.8
trainingYears = []
for i in range(yearStart, yearTrainEnd, 1):
trainingYears.append(str(i))
testYears = []
for i in range(yearTrainEnd, yearTestEnd, 1):
testYears.append(str(i))
trainDataMasterDict = {}
trainDataDict = {}
testDataDict = {}
for key in otherStates:
series = dfBasque[dfBasque['regionname'] == key]
trainDataMasterDict.update({key: series[trainingYears].values[0]})
# randomly hide training data
(trainData, pObservation) = tsUtils.randomlyHideValues(copy.deepcopy(trainDataMasterDict[key]), p)
trainDataDict.update({key: trainData})
testDataDict.update({key: series[testYears].values[0]})
series = dfBasque[dfBasque['regionname'] == basqueKey]
trainDataMasterDict.update({basqueKey: series[trainingYears].values[0]})
trainDataDict.update({basqueKey: series[trainingYears].values[0]})
testDataDict.update({basqueKey: series[testYears].values[0]})
trainMasterDF = pd.DataFrame(data=trainDataMasterDict)
trainDF = pd.DataFrame(data=trainDataDict)
testDF = pd.DataFrame(data=testDataDict)
# model
rscModel = RobustSyntheticControl(basqueKey, singvals, len(trainDF), probObservation=1.0, modelType='als',
otherSeriesKeysArray=otherStates)
# fit the model
rscModel.fit(trainDF)
# save the denoised training data
denoisedDF = rscModel.model.denoisedDF()
# predict - all at once
predictions = rscModel.predict(testDF)
# plot
yearsToPlot = range(yearStart, yearTestEnd, 1)
interventionYear = yearTrainEnd - 1
plt.plot(yearsToPlot, np.append(trainMasterDF[basqueKey], testDF[basqueKey], axis=0), color='red',
label='observations')
plt.plot(yearsToPlot, np.append(denoisedDF[basqueKey], predictions, axis=0), color='blue', label='predictions')
plt.axvline(x=interventionYear, linewidth=1, color='black', label='Intervention')
# plt.ylim((-1, 0))
legend = plt.legend(loc='upper right', shadow=True)
plt.title('Abadie et al. Basque Country Case Study - $p = %.2f$' % p)
plt.show()
def prop99(filename):
# CALIFORNIA PROP 99 STUDY
df = pd.read_csv(filename)
df = df[df['SubMeasureDesc'] == 'Cigarette Consumption (Pack Sales Per Capita)']
pivot = df.pivot_table(values='Data_Value', index='LocationDesc', columns=['Year'])
dfProp99 = pd.DataFrame(pivot.to_records())
allColumns = dfProp99.columns.values
states = list(np.unique(dfProp99['LocationDesc']))
years = np.delete(allColumns, [0])
caStateKey = 'California'
states.remove(caStateKey)
otherStates = states
yearStart = 1970
yearTrainEnd = 1989
yearTestEnd = 2015
singvals = 2
p = 1.0
trainingYears = []
for i in range(yearStart, yearTrainEnd, 1):
trainingYears.append(str(i))
testYears = []
for i in range(yearTrainEnd, yearTestEnd, 1):
testYears.append(str(i))
trainDataMasterDict = {}
trainDataDict = {}
testDataDict = {}
for key in otherStates:
series = dfProp99[dfProp99['LocationDesc'] == key]
trainDataMasterDict.update({key: series[trainingYears].values[0]})
# randomly hide training data
(trainData, pObservation) = tsUtils.randomlyHideValues(copy.deepcopy(trainDataMasterDict[key]), p)
trainDataDict.update({key: trainData})
testDataDict.update({key: series[testYears].values[0]})
series = dfProp99[dfProp99['LocationDesc'] == caStateKey]
trainDataMasterDict.update({caStateKey: series[trainingYears].values[0]})
trainDataDict.update({caStateKey: series[trainingYears].values[0]})
testDataDict.update({caStateKey: series[testYears].values[0]})
trainMasterDF = pd.DataFrame(data=trainDataMasterDict)
trainDF = pd.DataFrame(data=trainDataDict)
testDF = pd.DataFrame(data=testDataDict)
# model
rscModel = RobustSyntheticControl(caStateKey, singvals, len(trainDF), probObservation=1.0, modelType='als',
otherSeriesKeysArray=otherStates)
# fit the model
rscModel.fit(trainDF)
# save the denoised training data
denoisedDF = rscModel.model.denoisedDF()
# predict - all at once
predictions = rscModel.predict(testDF)
# plot
yearsToPlot = range(yearStart, yearTestEnd, 1)
interventionYear = yearTrainEnd - 1
plt.plot(yearsToPlot, np.append(trainMasterDF[caStateKey], testDF[caStateKey], axis=0), color='red',
label='observations')
plt.plot(yearsToPlot, np.append(denoisedDF[caStateKey], predictions, axis=0), color='blue', label='predictions')
plt.axvline(x=interventionYear, linewidth=1, color='black', label='Intervention')
legend = plt.legend(loc='lower left', shadow=True)
plt.title('Abadie et al. Prop 99 Case Study (CA) - $p = %.2f$' % p)
plt.show()
def synth_control_predictions(list_of_dfs,
threshold,
low_thresh,
title_text,
singVals=2,
savePlots=False,
ylimit=[],
logy=False,
exclude=[],
svdSpectrum=False,
showDonors=True,
do_only=[],
showstates=4,
animation=[],
figure=None,
axes=None,
donorPool=[],
silent=True,
showPlots=True,
mRSC=False,
lambdas=[1],
error_thresh=1,
yaxis='Cases',
FONTSIZE=20,
tick_spacing=30,
random_distribution=None):
#print('yo', list_of_dfs,'bo')
#print(len(list_of_dfs))
df = list_of_dfs[0]
if (donorPool):
otherStates = donorPool.copy()
else:
sizes = df.apply(pd.Series.last_valid_index)
sizes = sizes.fillna(0).astype(int)
otherStates = list(sizes[sizes > threshold].index)
if (exclude):
for member in exclude:
if (member in otherStates):
otherStates.remove(member)
if (do_only):
for member in exclude:
if (member in otherStates):
otherStates.remove(member)
for member in do_only:
if (member in otherStates):
otherStates.remove(member)
showstates = np.minimum(showstates, len(otherStates))
otherStatesNames = otherStates
otherStatesNames = [w.replace('-None', '') for w in otherStates]
for state in otherStatesNames:
state.replace("-None", "")
if not silent:
print(otherStates)
if (do_only):
#prediction_states = list(sizes[sizes.index.isin(do_only)].index)
prediction_states = do_only
if not silent:
print(prediction_states)
else:
prediction_states = list(sizes[(sizes > low_thresh)
& (sizes <= threshold)].index)
for state in prediction_states:
all_rows = list.copy(otherStates)
all_rows.append(state)
if not mRSC:
if random_distribution:
trainDF = df + random_distribution(df.shape)
trainDF = trainDF.iloc[:low_thresh, :]
else:
trainDF = df.iloc[:low_thresh, :]
else:
num_dimensions = len(lambdas)
trainDF = pd.DataFrame()
length_one_dimension = list_of_dfs[0].shape[0]
for i in range(num_dimensions):
trainDF = pd.concat([
trainDF, lambdas[i] * list_of_dfs[i].iloc[:low_thresh, :]
],
axis=0)
if not silent:
print(trainDF.shape)
testDF = df.iloc[low_thresh + 1:threshold, :]
rscModel = RobustSyntheticControl(state,
singVals,
len(trainDF),
probObservation=1.0,
modelType='svd',
svdMethod='numpy',
otherSeriesKeysArray=otherStates)
rscModel.fit(trainDF)
denoisedDF = rscModel.model.denoisedDF()
predictions = []
predictions = np.dot(testDF[otherStates].values,
rscModel.model.weights)
predictions_noisy = np.dot(testDF[otherStates].values,
rscModel.model.weights)
x_actual = df[state].index #range(sizes[state])
actual = df[state] #df.iloc[:sizes[state],:][state]
if (svdSpectrum):
(U, s, Vh) = np.linalg.svd((trainDF[all_rows]) -
np.mean(trainDF[all_rows]))
s2 = np.power(s, 2)
plt.figure(figsize=(8, 6))
plt.plot(s2)
plt.grid()
plt.xlabel("Ordered Singular Values", fontsize=FONTSIZE)
plt.ylabel("Energy", fontsize=FONTSIZE)
plt.title("Singular Value Spectrum", fontsize=FONTSIZE)
plt.show()
x_predictions = df.index[
low_thresh:low_thresh +
len(predictions)] #range(low_thresh,low_thresh+len(predictions))
model_fit = np.dot(trainDF[otherStates][:], rscModel.model.weights)
error = mse(actual[:low_thresh], model_fit)
if not silent:
print(state, error)
# if showPlots:
# plt.figure(figsize=(16,6))
ind = np.argpartition(rscModel.model.weights,
-showstates)[-showstates:]
topstates = [otherStates[i] for i in ind]
if showDonors:
axes[0].barh(otherStates,
rscModel.model.weights /
np.max(rscModel.model.weights),
color=list('rgbkymc'))
axes[0].set_title("Normalized weights for " +
str(state).replace("-None", ""),
fontsize=FONTSIZE)
ax = axes[-1] if showDonors else axes
if (ylimit):
ax.set_ylim(ylimit)
if (logy):
ax.set_yscale('log')
if (showPlots):
ax.plot(x_actual,
actual,
label='Actuals',
color='k',
linestyle='-')
ax.plot(x_predictions,
predictions,
label='Predictions',
color='r',
linestyle='--')
ax.plot(df.index[:low_thresh],
model_fit,
label='Fitted model',
color='g',
linestyle=':')
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
ax.axvline(x=df.index[low_thresh - 1],
color='k',
linestyle='--',
linewidth=4)
ax.grid()
if showDonors:
ax.set_title(title_text + " for " +
str(state).replace("-None", ""),
fontsize=FONTSIZE)
ax.set_xlabel("Days since Intervention", fontsize=FONTSIZE)
ax.set_ylabel(yaxis, fontsize=FONTSIZE)
ax.legend(['Actuals', 'Predictions', 'Fitted Model'],
fontsize=FONTSIZE)
else:
ax.tick_params(axis='both', which='major', labelsize=FONTSIZE)
ax.set_title(title_text + " for " +
str(state).replace("-None", ""),
fontsize=FONTSIZE)
ax.set_xlabel("Days since Intervention", fontsize=FONTSIZE)
ax.set_ylabel(yaxis, fontsize=FONTSIZE)
ax.legend(['Actuals', 'Predictions', 'Fitted Model'],
fontsize=FONTSIZE)
if (savePlots):
plt.savefig("../Figures/COVID/" + state + ".png")
if (animation):
animation.snap()
#pred_plot.remove()
elif (showPlots):
plt.show()
if (error < error_thresh):
return (dict(zip(otherStates, rscModel.model.weights)))
else:
print(state, error)
return (dict(
zip(otherStates, -50 * np.ones(len(rscModel.model.weights)))))
def synth_control_predictions(list_of_dfs,
threshold,
low_thresh,
title_text,
singVals=2,
savePlots=False,
ylimit=[],
xlimit=[],
logy=False,
exclude=[],
svdSpectrum=False,
showDonors=True,
do_only=[],
showstates=4,
animation=[],
figure=None,
axes=None,
donorPool=[],
silent=True,
showPlots=True,
mRSC=False,
lambdas=[1],
error_thresh=1,
yaxis='Cases',
FONTSIZE=20,
tick_spacing=30,
random_distribution=None,
check_nan=0,
return_permutation_distribution=False,
intervention_date_x_ticks=None):
df = list_of_dfs[0]
if (donorPool):
otherStates = donorPool.copy()
else:
sizes = df.apply(pd.Series.last_valid_index)
sizes = sizes.fillna(0).astype(int)
otherStates = list(sizes[sizes > threshold].index)
if (exclude):
for member in exclude:
if (member in otherStates):
otherStates.remove(member)
if (do_only):
for member in exclude:
if (member in otherStates):
otherStates.remove(member)
for member in do_only:
if (member in otherStates):
otherStates.remove(member)
showstates = np.minimum(showstates, len(otherStates))
otherStatesNames = otherStates
otherStatesNames = [w.replace('-None', '') for w in otherStates]
for state in otherStatesNames:
state.replace("-None", "")
if not silent:
print(otherStates)
if (do_only):
#prediction_states = list(sizes[sizes.index.isin(do_only)].index)
prediction_states = do_only
if not silent:
print(prediction_states)
else:
prediction_states = list(sizes[(sizes > low_thresh)
& (sizes <= threshold)].index)
if check_nan:
start = max(df[state].first_valid_index()
for state in prediction_states)
if low_thresh - start > check_nan:
start = low_thresh - check_nan
df = df.iloc[start:].reset_index(drop=True)
list_of_dfs = [
df.iloc[start:].reset_index(drop=True) for df in list_of_dfs
]
low_thresh -= start
otherStates = [
state for state in otherStates
if df[state].first_valid_index() == 0
]
print('final donorpool: ', otherStates)
for state in prediction_states:
all_rows = list.copy(otherStates)
all_rows.append(state)
if not mRSC:
if random_distribution:
trainDF = df + random_distribution(df.shape)
trainDF = trainDF.iloc[:low_thresh, :]
else:
trainDF = df.iloc[:low_thresh, :]
else:
num_dimensions = len(lambdas)
trainDF = pd.DataFrame()
length_one_dimension = list_of_dfs[0].shape[0]
for i in range(num_dimensions):
trainDF = pd.concat([
trainDF, lambdas[i] * list_of_dfs[i].iloc[:low_thresh, :]
],
axis=0)
if not silent:
print(trainDF.shape)
testDF = df.iloc[low_thresh + 1:threshold, :]
rscModel = RobustSyntheticControl(state,
singVals,
len(trainDF),
probObservation=1.0,
modelType='svd',
svdMethod='numpy',
otherSeriesKeysArray=otherStates)
rscModel.fit(trainDF)
denoisedDF = rscModel.model.denoisedDF()
predictions = []
predictions = np.dot(testDF[otherStates].fillna(0).values,
rscModel.model.weights)
predictions_noisy = np.dot(testDF[otherStates].fillna(0).values,
rscModel.model.weights)
predictions[predictions < 0] = 0
x_actual = df[state].index #range(sizes[state])
actual = df[state] #df.iloc[:sizes[state],:][state]
if (svdSpectrum):
(U, s, Vh) = np.linalg.svd((trainDF[all_rows]) -
np.mean(trainDF[all_rows]))
s2 = np.power(s, 2)
plt.figure(figsize=(8, 6))
plt.plot(s2)
plt.grid()
plt.xlabel("Ordered Singular Values", fontsize=FONTSIZE)
plt.ylabel("Energy", fontsize=FONTSIZE)
plt.title("Singular Value Spectrum", fontsize=FONTSIZE)
plt.show()
x_predictions = df.index[
low_thresh:low_thresh +
len(predictions)] #range(low_thresh,low_thresh+len(predictions))
model_fit = np.dot(trainDF[otherStates][:].fillna(0),
rscModel.model.weights)
model_fit[model_fit < 0] = 0
error = mse(actual[:low_thresh], model_fit)
if not silent:
print(state, error)
# if showPlots:
# plt.figure(figsize=(16,6))
ind = np.argpartition(rscModel.model.weights,
-showstates)[-showstates:]
topstates = [otherStates[i] for i in ind]
if showDonors:
axes[0].barh(otherStates,
rscModel.model.weights /
np.max(rscModel.model.weights),
color=list('rgbkymc'))
axes[0].set_title("Normalized weights for " +
str(state).replace("-None", ""),
fontsize=FONTSIZE)
axes[0].tick_params(axis='both', which='major', labelsize=FONTSIZE)
ax = axes[-1] if showDonors else axes
if (ylimit):
ax.set_ylim(ylimit)
if (xlimit):
ax.set_xlim(xlimit)
if (logy):
ax.set_yscale('log')
if (showPlots):
#if not
ax.plot(x_actual,
actual,
label='Actuals',
color='k',
linestyle='-')
ax.plot(x_predictions,
predictions,
label='Predictions',
color='r',
linestyle='--')
ax.plot(df.index[:low_thresh],
model_fit,
label='Fitted model',
color='g',
linestyle=':')
ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))
ax.axvline(x=df.index[low_thresh - 1],
color='k',
linestyle='--',
linewidth=4)
ax.grid()
ax.tick_params(axis='both', which='major', labelsize=FONTSIZE)
if title_text:
ax.set_title(title_text + " for " +
str(state).replace("-None", ""),
fontsize=FONTSIZE)
ax.set_xlabel("Days since intervention", fontsize=FONTSIZE)
ax.set_ylabel(yaxis, fontsize=FONTSIZE)
ax.legend(['Actuals', 'Predictions', 'Fitted Model'],
fontsize=FONTSIZE)
#pred_plot.remove()
#plt.show()
figure.canvas.draw()
if intervention_date_x_ticks:
labels = [item.get_text() for item in ax.get_xticklabels()]
x_labels = []
ts = (pd.to_datetime(intervention_date_x_ticks[state]))
#ts = pd.to_datetime(str(date))
for label in labels:
tmp_date = ts + datetime.timedelta(days=int(label))
x_labels.append(tmp_date.strftime('%Y-%m-%d'))
ax.set_xlabel("Date", fontsize=FONTSIZE)
#print(x_labels)
#int_date = (ts.strftime('%Y-%m-%d'))
#labels = list(df.index.values)
ax.set_xticklabels(x_labels, rotation=45)
if (savePlots):
plt.savefig("../Figures/COVID/" + state + '.pdf',
bbox_inches='tight')
if (animation):
animation.snap()
else:
plt.show()
if return_permutation_distribution:
# sklearn MSE is different from our MSE defined above; I'm not sure what our MSE is intended to represent, as I have never seen MSE defined in that way.
def find_ri(actual, model_fit, predictions):
return mean_squared_error(
actual[len(model_fit):len(model_fit) + len(predictions)],
predictions) / mean_squared_error(actual[:len(model_fit)],
model_fit)
out_dict = dict()
out_dict[state] = find_ri(
actual, model_fit,
predictions) # this only works when prediction_states has length 1
for state in otherStates:
donorPool = otherStates.copy()
donorPool.remove(state)
rscModel = RobustSyntheticControl(state,
singVals,
len(trainDF),
probObservation=1.0,
modelType='svd',
svdMethod='numpy',
otherSeriesKeysArray=donorPool)
rscModel.fit(trainDF)
actual = df[state].fillna(0)
model_fit = np.dot(trainDF[donorPool].fillna(0),
rscModel.model.weights)
predictions = np.dot(testDF[donorPool].fillna(0),
rscModel.model.weights)
out_dict[state] = find_ri(actual, model_fit, predictions)
return out_dict
if (error < error_thresh):
return (dict(zip(otherStates, rscModel.model.weights)))
else:
print(state, error)
return (dict(
zip(otherStates, -50 * np.ones(len(rscModel.model.weights)))))