def main():
#---------------LOAD PARAMETERS, INITIALIZE VARS---------------
results = []
para = params_setup("ridge") # "ridge" for ridge VAR
# Which variables are > thresh?
thresh = para.variable_threshold
# How far in the future are we predicting?
offset = para.horizon
# What is the window of previous timesteps we're looking at?
attn_length = para.attention_len
#---------------PREPARE DATA------------------
(X_cut, y_cut) = load_data(para)
num_timesteps = X_cut.shape[0]
num_features = X_cut.shape[1]
X = list()
for i in range(attn_length, num_timesteps - offset):
X.append(
(X_cut[i - attn_length:i, :]).flatten()
) # must flatten (timesteps, features) into 1D b/c model only takes up to 2D
X = np.array(X)
y = y_cut[attn_length + offset:num_timesteps]
# Split into training and testing
cutoff_idx = math.floor(X.shape[0] * 0.8)
X_train = X[0:cutoff_idx, :]
y_train = y[0:cutoff_idx]
X_test = X[cutoff_idx:, :]
y_test = y[cutoff_idx:]
print("X_train shape:", X_train.shape)
print("y_train shape:", y_train.shape)
print("X_test shape:", X_test.shape)
print("y_test shape:", y_test.shape)
for aa in ALPHAS:
clf = linear_model.Ridge(alpha=aa, normalize=True)
clf.fit(X_train, y_train)
#0 rated emotion signal
#1-13 MFCCs
#14-26 dMFCCs
#27-39 ddMFCCs
#40 clarity
#41 brightness
#42 key_strength
#43 rms
#44 centroid
#45 spread
#46 skewness
#47 kurtosis
#48-59 chroma
#60 mode
#61 compress
#62 hcdf
#63 flux
#64-74 lpcs
# Determine which variables (timesteps, since this is AR) are most important
inds = [
j for (i, j) in zip(clf.coef_, range(len(clf.coef_)))
if abs(i) >= thresh
]
inds_mod = [x % num_features for x in inds]
# Get RMSE
RMSE = ((len(y_test)**-1) * sum((clf.predict(X_test) - y_test)**2))**.5
print("RMSE for alpha " + str(aa) + ": " + (str)(RMSE))
plt.plot(range(len(y_test)), clf.predict(X_test))
plt.plot(range(len(y_test)), y_test)
#plt.show()
#plt.pause(3)
#plt.close()
results.append(
Result(aa, RMSE, clf.coef_, sorted(inds_mod), clf.intercept_))
min_rmse = results[0].RMSE
min_idx = 0
for i in range(0, len(results)):
if (results[i].RMSE < min_rmse):
min_rmse = results[i].RMSE
min_idx = i
print("Minimum RMSE: " + str(min_rmse) + " for alpha=" +
str(results[min_idx].alpha))
# ----------------- WRITE RESULT OF BEST ALPHA TO FILE -------------------
best_result = results[min_idx]
with open(para.output_filename, 'w') as f:
f.write("RMSE: " + (str)(best_result.RMSE))
f.write("\nAlpha: " + (str)(best_result.alpha))
f.write("\nCoefficients:\n")
f.write(np.array2string(best_result.coefs, threshold=np.nan))
f.write("\nCoefficient indices over threshold " +
str(para.variable_threshold) + ":\n")
f.write(' '.join(str(x) for x in best_result.coef_indices))
f.write("\nTotal number of coefficients over threshold: " +
str(len(best_result.coef_indices)))
f.write("\nRegression bias term: " + str(best_result.bias))
# Write overall RMSE for each alpha as well
f.write("\n\nRMSEs for each alpha:\n")
for result in results:
f.write("Alpha: " + str(result.alpha) + ",\tRMSE: " +
str(result.RMSE) + "\n")
print("Successfully wrote results to file " + para.output_filename)
def main():
para = params_setup("ar") # "ar" for autoregression
clf = linear_model.LinearRegression(normalize=True)
#Which variables are > thresh?
thresh = para.variable_threshold
#How far in the future are we predicting?
offset = para.horizon
#What is the length of the autoregression
attn_length = para.attention_len
(X_cut, y_cut) = load_data(para)
#Make the autoregressive IVs
y_regress = list()
for i in range(offset + attn_length, len(y_cut)):
y_regress.append(y_cut[i - attn_length - offset:i - offset])
y_regress = np.array(y_regress)
#Split into training and testing
if (para.test_set_at_beginning):
cutoff_idx = math.floor(y_regress.shape[0] * 0.2)
X_test = y_regress[0:(cutoff_idx - attn_length - offset), :]
y_test = y_cut[attn_length + offset:cutoff_idx]
X_train = y_regress[cutoff_idx - attn_length - offset:, :]
y_train = y_cut[cutoff_idx:]
else:
cutoff_idx = math.floor(y_regress.shape[0] * 0.8)
X_train = y_regress[0:(cutoff_idx - attn_length - offset), :]
y_train = y_cut[attn_length + offset:cutoff_idx]
X_test = y_regress[cutoff_idx - attn_length - offset:, :]
y_test = y_cut[cutoff_idx:]
print("X_train shape:", X_train.shape)
print("y_train shape:", y_train.shape)
clf.fit(X_train, y_train)
# 0 rated emotion signal
# 1-13 MFCCs
# 14-26 dMFCCs
# 27-39 ddMFCCs
# 40 clarity
# 41 brightness
# 42 key_strength
# 43 rms
# 44 centroid
# 45 spread
# 46 skewness
# 47 kurtosis
# 48-59 chroma
# 60 mode
# 61 compress
# 62 hcdf
# 63 flux
# 64-74 lpcs
#Determine which variables (timesteps, since this is AR) are most important
inds = [
j for (i, j) in zip(clf.coef_, range(len(clf.coef_)))
if abs(i) >= thresh
]
# print(sorted(inds))
# print(sum(abs(clf.coef_)>thresh))
# print((abs(clf.coef_)>thresh))
# print(nlargest(3, clf.coef_))
# print(clf.intercept_)
#Get RMSE
RMSE = ((len(y_test)**-1) * sum((clf.predict(X_test) - y_test)**2))**0.5
print("RMSE: " + (str)(RMSE))
if (para.show_plots):
plt.plot(range(len(y_test[:-2])), clf.predict(X_test[:][:-2]))
plt.plot(range(len(y_test[:-2])), y_test[:-2])
plt.title('Autoregression predictions vs truth')
plt.legend(['predictions', 'truth'], loc='upper right')
plt.show()
with open(para.output_filename, 'w') as f:
f.write("RMSE: " + (str)(RMSE))
f.write("\nCoefficients:\n")
f.write(np.array2string(clf.coef_, threshold=np.nan))
f.write("\nCoefficient indices over threshold " +
str(para.variable_threshold) + ":\n")
f.write(' '.join(str(x) for x in sorted(inds)))
f.write("\nTotal number of coefficients over threshold: " +
str(sum(abs(clf.coef_) > thresh)))
f.write("\nRegression bias term: " + str(clf.intercept_))
print("Successfully wrote results to file " + para.output_filename)