def make_score_heatmap(parameters, param1, param2, grid_model, dataset):
"""
Produce heatmap of mean scores for each combination of the parameters
Parameters
----------
parameters : dictionary of lists
Grid values for the Grid Search algorithm
param1 : string
The parameter C
param2 : string
The parameter gamma
grid_model : GridSearchCV object
Trained model
dataset : string
Type of dataset: 'All' or 'Reduced'
Returns
-------
Produce chart files in directory ../../target/visualization
"""
scores = [score for score in grid_model.cv_results_['mean_test_score']]
scores = np.array(scores).reshape(6, 6)
fig, ax = plt.subplots()
im, cbar = heatmap(scores,
parameters[param1],
parameters[param2],
ax=ax,
cmap="YlGn",
cbarlabel="Mean Scores")
annotate_heatmap(im, valfmt="{x:.2f}")
ax.set_xlabel(param2)
ax.set_ylabel(param1)
title = dataset + ' Datapoints GridSearchCV Mean Scores'
ax.set_title(title)
fig.tight_layout()
outfile = '../../target/visualization/' + title + '.png'
plt.savefig(outfile, format="png")
def optimal_play_chart(player):
data = np.genfromtxt('../HU_push_fold/nash_' + player + '.csv',
delimiter=',',
dtype=float)
data = np.reshape(data, (13, 13))
fig, ax = plt.subplots()
title = 'Small blind optimal play' if player == 'sb' else 'Big blind optimal play'
ax.set_title(title)
axis_labels = [
"A", "K", "Q", "J", "T", "9", "8", "7", "6", "5", "4", "3", "2"
]
im, cbar = heatmap.heatmap(data,
axis_labels,
axis_labels,
ax=ax,
cmap="YlGn",
cbarlabel="Big Blinds")
texts = heatmap.annotate_heatmap(im, valfmt="{x:.1f}")
fig.tight_layout()
fig.savefig('./' + player + '_range.svg')
# Selezione dati per Adriatica (SS1), Aurelia (SS16),
# A1 (del Sole), Torino-Trieste (A4) e Raccordo anulare di Roma
adriatica = data[data['CODICE'] == 'SS01601'][mesi]
a1 = data[data['CODICE'] == 'AA00101'][mesi]
aurelia = data[data['CODICE'] == 'SS00101'][mesi]
a4 = data[data['CODICE'] == 'AA00401'][mesi]
a90 = data[data['CODICE'] == 'AA09001'][mesi]
adriatica = aci_utils.sum_columns(adriatica)
a1 = aci_utils.sum_columns(a1)
aurelia = aci_utils.sum_columns(aurelia)
a4 = aci_utils.sum_columns(a4)
a90 = aci_utils.sum_columns(a90)
df = pd.DataFrame([a1, adriatica, aurelia, a4, a90], [
'A1 Milano-Roma-Napoli', 'SS16 Adriatica', 'SS1 Aurelia',
'A4 Torino Trieste', 'A90 Raccordo Anulare'
])
fig, ax = plt.subplots()
im = H.heatmap(df,
df.index,
df.columns,
ax=ax,
cmap="OrRd",
xticks_rotated=True,
cbar_visible=False)
texts = H.annotate_heatmap(im, valfmt="{x}")
fig.tight_layout()
plt.show()
es_score_n = []
#others_n = []
for N in test_sizes:
select = df.loc[(df["P"] == P) & (df["N"] == N)]
if selected_dataset:
select = select.loc[select["Dataset"] == datasets[selected_dataset-1]]
if P in test_sizes[2:] and N in test_sizes[2:]:
overall = select.loc[:, "ROC(auc)"].mean()
else:
overall = select.loc[:, "Balanced accuracy"].mean()
es_score_n.append(overall)
score_dict["es_score"].append(es_score_n)
test_sizes_p = [x+"P" for x in test_sizes]
test_sizes_n = [x+"N" for x in test_sizes]
ext = "hepmark_es_svm_"+str(selected_dataset-1)+".pdf" if selected_dataset else "hepmark_es_svm.pdf"
# Uncomment to Latexify the heatmap
#latexify(columns=2)
# Generate a Heatmap
scores = np.array(score_dict["es_score"])
fig, ax = plt.subplots()
im, cbar = heatmap.heatmap(scores, test_sizes_p, test_sizes_n, ax=ax,
vmin = 0.0, vmax = 1.0, cmap=cm.Reds, cbarlabel="score [AUC / Acc]")
texts = heatmap.annotate_heatmap(im, valfmt="{x:.2f}")
fig.tight_layout()
plt.show()
fig.savefig("C:/Users/Vegard/Desktop/Master/Mastersproject/Plots/analyze/"+ext)
prov_chap = np.zeros((len(dep_options), len(chap_set)))
for aux in range(len(dep)):
i = dep_options.index(dep[aux])
for j in icd9_chap([x[:3] for x in labels_cid[aux]]):
if j < 19: prov_chap[i][j - 1] += 1
fig, ax = plt.subplots(figsize=(20, 25))
im = heatmap(prov_chap,
dep_options,
chap_set,
ax=ax,
cmap="Reds",
cbarlabel="Prevalence")
text = annotate_heatmap(im, valfmt="{x:.0f}")
plt.ylabel('Department', fontsize='large')
ax.set_xlabel('ICD-9 chapters', fontsize='large')
ax.xaxis.set_label_position('top')
fig.tight_layout()
fig.savefig('heatmap_dep.png', dpi=250)
#%%
print('- Age Groups:')
idade = [int(line[2]) for line in texts]
for i in range(len(idade)):
if idade[i] < 5: idade[i] = 0
elif idade[i] < 15: idade[i] = 1
x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
return np.ma.masked_array(np.interp(value, x, y))
# create a matplotlib figure for the Q table
fig = plt.figure('Q-table')
ax = fig.add_subplot(111)
im, cbar = heatmap(q_table[0],
list(range(numDistSensorStates)),
action_names,
ax=ax,
cmap="RdBu",
cbarlabel="Q table",
norm=DivergingNorm(vcenter=0.0))
#norm=MidpointNormalize(midpoint=0))
texts = annotate_heatmap(im, valfmt="{x:.1f}")
plt.draw()
#fig, ax = plt.subplots()
total_reward = 0
start_time = datetime.datetime.now() # log start of episode
rewards = np.zeros(200)
for step in range(0, steps - 1):
# Use epsilon greedy policy based on Q table
if N0 == 0:
epsilon = 0
else:
epsilon = N0 / (N0 + step)
if random.random() > epsilon:
a = np.argmax(
q_table[st,
data = pd.read_csv(path + str(year) + ".txt",
sep="\t",
error_bad_lines=False,
engine='python')
else:
data = pd.read_csv(path + str(year) + ".txt",
sep="\t",
encoding="latin1")
natura_incidente = data['natura_incidente']
natura_incidente_labels = label_utils.join_labels(
natura_incidente,
"dataset/incidenti/istat/Classificazioni/natura_incidente.csv"
).value_counts(normalize=True)
natura_incidente_labels = natura_incidente_labels[tipo_incidenti]
inc_per_anno[str(year)] = pd.Series(natura_incidente_labels)
fig, ax = plt.subplots()
im = H.heatmap(inc_per_anno,
inc_per_anno.index,
inc_per_anno.columns,
ax=ax,
cmap="OrRd",
cbar_visible=False)
texts = H.annotate_heatmap(im)
fig.tight_layout()
plt.show()
a2 = a2 + o[2]
a3 = a3 + o[3]
return [a1 / len(obs), a2 / len(obs), a3 / len(obs)]
matrix = []
for room in Rooms:
observations = []
for d in Data:
if d[0] == room:
observations.append(d)
matrix.append(getAvg(observations))
measures = np.array(matrix)
fig, ax = plt.subplots(figsize=(5, 8))
im, cbar = heatmap(measures,
Rooms,
Accesspoints,
ax=ax,
cmap="Blues",
cbarlabel="Signal strength in dBm ")
texts = annotate_heatmap(im, valfmt="{x:.1f} ")
fig.tight_layout()
plt.savefig("Plots/AvgSignalPrLocation.svg",
bbox_inches="tight") # 'tight' makes room for x-axis labels
plt.show()