def test(out_dir, x_train, y_train, max_trees, step, min_trees=None, silent=False):
''' Make a plot for sample_txt of number of trees vs OOB error rates '''
if not silent: print 'Testing OOB error rate per number of trees...'
oob_errors = []
if not min_trees:
min_trees = step
n_trees = range(min_trees, max_trees + 1, step)
n_tests = len(n_trees)
for i, n in enumerate(n_trees):
rf_model = forest.train_rf_regressor(x_train, y_train, ntrees=n, njobs=1)
oob_errors.append(1 - rf_model.oob_score_)
if not silent:
print 'Testing %s of %s models with %s trees: %.3f' % (i + 1, n_tests, n, rf_model.oob_score_)
plt.plot(n_trees, oob_errors, '-')
plt.axis([0, max_trees, 0, 1])
plt.xlabel('Number of Trees')
plt.ylabel('Out of Bag Error Rate')
plt.title('Number of Decision Trees vs. OOB Error Rate')
out_png = os.path.join(out_dir, 'ntrees_vs_oob_error.png')
plt.savefig(out_png)
plt.clf()
if not silent: print 'Plot PNG written to : ', out_png, '\n'
return [n_trees, oob_errors]
def test(out_dir, x_train, y_train, max_trees, step):
''' Make a plot for sample_txt of number of trees vs OOB error rates '''
print 'Testing OOB error rate per number of trees...'
oob_errors = []
n_trees = range(50, max_trees + 1, step)
n_tests = max_trees / step
for i, n in enumerate(n_trees):
rf_model = forest.train_rf_regressor(x_train, y_train, ntrees=n)
#import pdb; pdb.set_trace()
oob_errors.append(1 - rf_model.oob_score_)
print 'Testing %s of %s models with %s trees: %.3f' % (
i + 1, n_tests, n, rf_model.oob_score_)
plt.plot(n_trees, oob_errors, '-')
plt.axis([0, max_trees, 0, 1])
plt.xlabel('Number of Trees')
plt.ylabel('Out of Bag Error Rate')
plt.title('Number of Decision Trees vs. OOB Error Rate')
out_png = os.path.join(out_dir, 'ntrees_vs_oob_error.png')
plt.savefig(out_png)
plt.clf()
print 'Plot PNG written to : ', out_png, '\n'
def main(params):
# Read params and make variables from text
inputs = forest.read_params(params)
for i in inputs:
exec("{0} = str({1})").format(i, inputs[i])
# Check that variables were specified in params
try:
str_check = sample_txt, target_col, var_txt, out_dir
except NameError as e:
print ''
missing_var = str(e).split("'")[1]
msg = "Variable '%s' not specified in param file:\n%s" % (missing_var,
params)
raise NameError(msg)
# Make optional numeric arguments numeric
if 'n_trees' in locals():
n_trees = int(n_trees)
else:
n_trees = 50
if 'n_jobs' in locals():
n_jobs = int(n_jobs)
else:
n_jobs = 12
if 'max_depth' in locals():
max_depth = int(max_depth)
else:
max_depth = None
# Raise an error if var_txt doesn't exist. Otherwise, just read it in
if not os.path.exists(var_txt):
print ''
msg = 'var_text path specified does not exist:\n%s\n\n' % var_txt
raise IOError(msg)
df_var = pd.read_csv(var_txt, sep='\t', index_col='var_name')
# Make the output directory
now = datetime.now()
date_str = str(now.date()).replace('-', '')
time_str = str(now.time()).replace(':', '')[:4]
stamp = '{0}_{1}_{2}'.format('susceptibility', date_str, time_str)
out_dir = os.path.join(out_dir, stamp)
os.makedirs(
out_dir
) # With a timestamp in dir, no need to check if it already exists
shutil.copy2(params,
out_dir) #Copy the params so the parameters used are saved
#shutil.copy2(var_txt, out_dir)
# Read in training samples
df_train = pd.read_csv(sample_txt, sep='\t', index_col='obs_id')
# Check that df_train has exactly the same columns as variables specified in df_vars
train_columns = df_train.columns.tolist()
unmatched_vars = [v for v in df_var.index if v not in train_columns]
if len(unmatched_vars) != 0:
unmatched_str = '\n'.join(unmatched_vars)
msg = 'Columns not in sample_txt but specified in params:\n' + unmatched_str
raise NameError(msg)
# Sort the predictors in alphabetical order so that train columns can be in the same order as the predict array when
# predicting later on
predict_cols = sorted(
np.unique(
[c for c in df_train.columns for v in df_var.index if v in c]))
df_var = df_var.sort_index()
x_train = df_train.reindex(columns=predict_cols)
y_train = df_train[target_col]
rf_model = forest.train_rf_regressor(x_train,
y_train,
ntrees=n_trees,
njobs=n_jobs,
max_depth=max_depth)
df_var['importance'] = rf_model.feature_importances_
rf_path = os.path.join(out_dir, 'regressor_model_%s' % stamp)
forest.save_rfmodel(rf_model, rf_path)
oob_score = round(rf_model.oob_score_, 3)
out_var_txt = os.path.join(out_dir, os.path.basename(var_txt))
df_var.to_csv(out_var_txt, sep='\t')
# Record params in inventory text file
df_inv = pd.read_csv(inventory_txt, sep='\t')
col_str = re.sub('[\]\[\'\"]', '', str(predict_cols))
raster_res = sample_txt.split('_')[-2].replace('m', '')
df_inv = df_inv.append(pd.DataFrame([[
stamp, oob_score, '', '', '', '',
len(df_train), raster_res, col_str
]],
columns=df_inv.columns),
ignore_index=True)
existing_models = fnmatch.filter(os.listdir(os.path.dirname(out_dir)),
'susc*')
df_inv = df_inv[df_inv.stamp.isin(existing_models)]
df_inv.to_csv(inventory_txt, sep='\t', index=False)
print 'Random Forest Regressor model written to:\n', rf_path
print '\nOOB score: ', oob_score
print 'Relative importance:'
print df_var.importance
def main(params):
# Read params and make variables from text
inputs = forest.read_params(params)
for i in inputs:
#import pdb; pdb.set_trace()
exec("{0} = str({1})").format(i, inputs[i])
# Check that variables were specified in params
try:
str_check = sample_txt, target_col, var_txt, out_dir
except NameError as e:
print ''
missing_var = str(e).split("'")[1]
msg = "Variable '%s' not specified in param file:\n%s" % (missing_var,
params)
raise NameError(msg)
# Make optional numeric arguments numeric
if 'n_trees' in locals():
n_trees = int(n_trees)
else:
n_trees = 200
if 'n_jobs' in locals():
n_jobs = int(n_jobs)
else:
n_jobs = 1
if 'max_depth' in locals():
max_depth = int(max_depth)
else:
max_depth = None
# Raise an error if var_txt doesn't exist. Otherwise, just read it in
if not os.path.exists(var_txt):
print ''
msg = 'var_text path specified does not exist:\n%s\n\n' % var_txt
raise IOError(msg)
df_var = pd.read_csv(var_txt, sep='\t', index_col='var_name')
# Make the output directory
now = datetime.now()
date_str = str(now.date()).replace('-', '')
time_str = str(now.time()).replace(':', '')[:4]
if not 'out_dirname' in locals(): out_dirname = target_col
stamp = '{0}_{1}_{2}'.format(out_dirname, date_str, time_str)
out_dir = os.path.join(out_dir, stamp)
os.makedirs(
out_dir
) # With a timestamp in dir, no need to check if it already exists
shutil.copy2(params,
out_dir) #Copy the params so the parameters used are saved
shutil.copy2(sample_txt, out_dir)
# Read in training samples
df_train = pd.read_csv(sample_txt, sep='\t', index_col='obs_id')
# Check that df_train has exactly the same columns as variables specified in df_vars
train_columns = df_train.columns.tolist()
unmatched_vars = [v for v in df_var.index if v not in train_columns]
if len(unmatched_vars) != 0:
unmatched_str = '\n'.join(unmatched_vars)
msg = 'Columns not in sample_txt but specified in params:\n' + unmatched_str
raise NameError(msg)
# Sort the predictors in alphabetical order so that train columns can be in the same order as the predict array when
# predicting later on
predict_cols = sorted(
np.unique([c for c in df_train.columns if c in df_var.index]))
predict_cols = [c for c in predict_cols if c in df_var.index]
if target_col in predict_cols: predict_cols.remove(target_col)
df_var = df_var.sort_index()
if 'constant_vars' in inputs:
constant_vars = sorted([i.strip() for i in constant_vars.split(',')])
unmatched_vars = [v for v in constant_vars if v not in train_columns]
if len(unmatched_vars) != 0:
unmatched_str = '\n'.join(unmatched_vars)
msg = 'Columns not in sample_txt but specified in params:\n' + unmatched_str
raise NameError(msg)
predict_cols += constant_vars
x_train = df_train.reindex(columns=predict_cols)
y_train = df_train[target_col]
rf_model = forest.train_rf_regressor(x_train,
y_train,
ntrees=n_trees,
njobs=n_jobs,
max_depth=max_depth)
if 'constant_vars' in inputs:
for v in constant_vars:
df_var = df_var.append(pd.Series(name=v))
importance = rf_model.feature_importances_
df_var['importance'] = importance
df_var['rank'] = [
int(r) for r in df_var.importance.rank(method='first', ascending=False)
]
out_txt = os.path.join(out_dir, '%s_importance.txt' % stamp)
rf_path = os.path.join(out_dir, 'regressor_model_%s' % stamp)
forest.save_rfmodel(rf_model, rf_path)
oob_score = round(rf_model.oob_score_, 3)
out_var_txt = os.path.join(out_dir, os.path.basename(var_txt))
df_var.to_csv(out_var_txt, sep='\t')
# Record params in inventory text file
if 'inventory_txt' in inputs:
df_inv = pd.read_csv(inventory_txt, sep='\t')
cols = df_inv.columns
try:
res = int(re.search('[0-9]{1,2}', out_dirname).group())
except:
res = None
df_inv = df_inv.append(pd.DataFrame([{
'stamp': stamp,
'temporal_res': res,
'oob_score': oob_score,
'auc': None,
'rmse': None,
'rmse_n': None,
'rmse_p': None,
'n_samples': len(df_train),
'n_trees': n_trees
}]),
ignore_index=True)
df_inv = df_inv.reindex(columns=cols)
existing_models = fnmatch.filter(os.listdir(os.path.dirname(out_dir)),
'*res*')
df_inv = df_inv[df_inv.stamp.isin(existing_models)]
df_inv.to_csv(inventory_txt, sep='\t', index=False)
print 'Random Forest Regressor model written to:\n', rf_path
print '\nOOB score: ', oob_score
print 'Relative importance:'
print df_var.importance.sort_values(ascending=False)