def agglo_logit_calc(Xps1, Yps1, nonmusic_subreddits):
""" Handles fitting and scoring of the agglomeration->logistic regression
machine learning scheme.
"""
Xps1 = Xps1.toarray()
logit = LogisticRegression()
(n_samples_1, _) = Xps1.shape
n_folds = 4
rand = 0
kf = KFold(n_samples_1, n_folds=n_folds, shuffle=True, random_state=rand)
logit_1 = 0.0
logit_20 = 0.0
n_lo = 1
n_hi = 155
step = 1
n_groups_gen = range(n_lo, n_hi + 1, step)
agglo_1s = [0.0 for _ in n_groups_gen]
agglo_20s = [0.0 for _ in n_groups_gen]
params = np.empty([len(n_groups_gen), n_folds], dtype=object)
logit_params = []
for i_fold, (train, test) in enumerate(kf):
print i_fold
logit.fit(Xps1[train], Yps1[train])
logit_params.append(logit.coef_)
logit_1 += 100.0 * logit.score(Xps1[test], Yps1[test])
(Xps20_test, Yps20_test) = prune_sparse_samples(Xps1[test], Yps1[test], threshold=20)
(Xps20_test, Yps20_test) = balance_data(Xps20_test, Yps20_test)
logit_20 += 100.0 * logit.score(Xps20_test, Yps20_test)
for j, n_groups in enumerate(n_groups_gen):
agglo = phi_agglomerate(N=n_groups).fit(Xps1[train], Yps1[train])
Xagglo_train_1, _ = agglo.transform(Xps1[train])
Xagglo_test_1, _ = agglo.transform(Xps1[test])
Xagglo_test_20, _ = agglo.transform(Xps20_test)
logit.fit(Xagglo_train_1, Yps1[train])
params[j][i_fold] = logit.coef_
agglo_1s[j] += 100.0 * logit.score(Xagglo_test_1, Yps1[test]) / n_folds
agglo_20s[j] += 100.0 * logit.score(Xagglo_test_20, Yps20_test) / n_folds
logit_1 /= n_folds
logit_20 /= n_folds
return (n_lo, n_hi, logit_1, logit_20, n_groups_gen, agglo_1s, agglo_20s, params, logit_params)
X = X.tocsr()
nonmusic_subreddits = array(nonmusic_subreddits, dtype=object)
(X, Y, genres) = kill_outcome(X, Y, genres, 'classical')
(X, Y, genres) = kill_outcome(X, Y, genres, 'electronic')
# Delete those predictors I failed to exclude when I created the pickle.
# Delete any predictors which are empty after killing outcomes
(X, nonmusic_subreddits) = sanitise_predictors(X, nonmusic_subreddits,
music_subreddits)
(Xps1, Yps1) = prune_sparse_samples(X, Y, threshold=1)
# (Xps20, Yps20) = prune_sparse_samples(X, Y, threshold=20)
(Xps1, Yps1) = balance_data(Xps1, Yps1)
# (Xps20, Yps20) = balance_data(Xps20, Yps20)
###########
# Plots #
###########
plot_LDA_histogram(Xps1, Xps20, Yps1, Yps20)
plot_sparsity(Xps1, Yps1)
plot_agglo_logit(Xps1, Yps1, nonmusic_subreddits)
plot_RBM(Xps1, Yps1)
graph_music_taste(Xps1, Yps1, nonmusic_subreddits)
def plot_RBM(Xps1, Yps1):
""" Produce a plot of RBM classification accuracy and model variation
"""
######################
# Stat/create data #
######################
n_lo = 10
n_hi = 140
N_range = range(n_lo, n_hi + 1, 10)
rand = 0
n_folds = 4
BRBMs = get_BRBMs(Xps1, Yps1, N_range, rand, n_folds)
(n_samples_1, n_features) = Xps1.shape
kf = KFold(n_samples_1, n_folds=n_folds, shuffle=True, random_state=rand)
#################
# Test models #
#################
logit = LogisticRegression()
logit_score = [0.0 for i in N_range]
logit_score_20 = [0.0 for i in N_range]
logit_params = []
params = np.empty([len(N_range), n_folds], dtype=object)
logit_params = []
logit_1 = 0.0
logit_20 = 0.0
for j_fold, (train, test) in enumerate(kf):
(Xps20_test, Yps20_test) = prune_sparse_samples(Xps1[test], Yps1[test], threshold=20)
(Xps20_test, Yps20_test) = balance_data(Xps20_test, Yps20_test)
for i, N in enumerate(N_range):
rbm = BRBMs[i][j_fold]
Xps1_train_trans = rbm.transform(Xps1[train])
logit.fit(Xps1_train_trans, Yps1[train])
params[i][j_fold] = logit.coef_
Xps1_test_trans = rbm.transform(Xps1[test])
logit_score[i] += 100.0 * logit.score(Xps1_test_trans, Yps1[test]) / n_folds
Xps20_test_trans = rbm.transform(Xps20_test)
logit_score_20[i] += 100.0 * logit.score(Xps20_test_trans, Yps20_test) / n_folds
logit.fit(Xps1[train], Yps1[train])
logit_params.append(logit.coef_)
logit_1 += (100.0 * logit.score(Xps1[test], Yps1[test])) / n_folds
logit_20 += (100.0 * logit.score(Xps20_test, Yps20_test)) / n_folds
############################################
# Plot - subplot 1 - prediction accuracy #
############################################
plot_n_lo = 0
plot_n_hi = n_hi
snscol = sns.color_palette("Set1", n_colors=8, desat=0.5)
labelfontsize = 16
linewidth = 2
fig = plt.figure(figsize=(10, 4.0))
fig.add_subplot(121)
plt.tight_layout(pad=2, w_pad=5)
plt.title("Model accuracy", size=22)
plt.xlabel("Number of hidden units", size=labelfontsize)
plt.ylabel("Correct predictions (%)", size=labelfontsize)
plt.plot(N_range, logit_score, label="RBM features", linewidth=linewidth, color=snscol[0])
plt.plot(N_range, logit_score_20, label=u"RBM features (≥20 subreddits)", linewidth=linewidth, color=snscol[1])
plt.plot(
[plot_n_lo, plot_n_hi],
[logit_1, logit_1],
label="No RBM",
linestyle=("dashed"),
linewidth=linewidth,
color=snscol[0],
)
plt.plot(
[plot_n_lo, plot_n_hi],
[logit_20, logit_20],
label=u"No RBM (≥20 subreddits)",
linestyle=("dashed"),
linewidth=linewidth,
color=snscol[1],
)
axes = plt.gca()
axes.set_xlim(plot_n_lo, plot_n_hi)
axes.set_ylim(60, 72)
plt.legend(fontsize=12.5, loc=4)
#######################################
# Plot - subplot 2 - Parameter RMSD #
#######################################
fig.add_subplot(122)
plt.title("Model instability", size=22)
plt.xlabel("Number of hidden units", size=labelfontsize)
plt.ylabel("Mean parameter fluctuations", size=labelfontsize)
mrmsds = []
# var params is structured as:
# params[k][j[i] = the ith model parameter of the jth model (jth fold in the
# cross-validation) in the kth number of predictor groups
for k, param_sets in enumerate(params):
mrmsd = get_mrmsd(param_sets)
mrmsds.append(mrmsd)
mrmsd_logit = get_mrmsd(logit_params)
plt.plot(N_range, mrmsds, linewidth=linewidth, color=snscol[2], label="RBM features")
plt.plot(
[plot_n_lo, plot_n_hi],
[mrmsd_logit, mrmsd_logit],
label="No RBM",
linestyle=("dashed"),
linewidth=linewidth,
color=snscol[2],
)
plt.legend(fontsize=12.5, loc=1)
axes = plt.gca()
axes.set_xlim(plot_n_lo, plot_n_hi)
axes.set_ylim(0.00, 0.45)
plt.savefig("README_figs/RBMs_logit.svg")
