def base(
use_filter="default",
data_path="~/data/faons/latest.csv",
filter_name="default.csv",
participant_subset="",
drop_metadata=True,
drop=[],
clean=7,
components=5,
facecolor="#ffffff",
):
data_path = path.expanduser(data_path)
filter_path = path.join(path.dirname(path.realpath(__file__)), "filters", filter_name)
filters = pd.read_csv(
filter_path, index_col=0, header=None
).transpose() # transpose filters because of .csv file formatting, specify index_col to not get numbered index
all_data = pd.read_csv(data_path)
all_data = all_data[map(lambda y: len(set(y)) > clean, np.array(all_data))]
# drops metadata
if drop_metadata == True:
all_data = all_data.drop(filters["metadata"][pd.Series.notnull(filters["metadata"])], axis=1)
# compile list of column names to be dropped:
drop_list = []
for drop_item in drop:
drop_list += list(filters[drop_item][pd.Series.notnull(filters[drop_item])])
drop_list = list(
set(drop_list)
) # get unique column names (the list may contain duplicates if overlaying multiple filters)
all_data = all_data.drop(drop_list, axis=1)
if participant_subset == "odd":
keep_rows = all_data.index.values[1::2]
filtered_data = all_data.ix[keep_rows]
elif participant_subset == "even":
keep_rows = all_data.index.values[0::2]
filtered_data = all_data.ix[keep_rows]
elif participant_subset == "male":
filtered_data = all_data[all_data["My legal gender:"] == "Male"]
elif participant_subset == "female":
filtered_data = all_data[all_data["My legal gender:"] == "Female"]
else:
filtered_data = all_data
# convert to correct type for analysis:
filtered_data_array = np.array(filtered_data, dtype="float64")
filtered_data_array = filtered_data_array / 100
pca = PCA()
S_pca_ = pca.fit_transform(filtered_data_array)
fa = FactorAnalysis(svd_method="lapack")
S_fa_ = fa.fit_transform(filtered_data_array)
ica = FastICA(n_components=components, max_iter=20000, tol=0.00001)
S_ica_ = ica.fit_transform(filtered_data_array) # Estimate the sources
load = ica.mixing_
remapped_cmap = remappedColorMap(
cm.PiYG,
start=(np.max(load) - abs(np.min(load))) / (2 * np.max(load)),
midpoint=abs(np.min(load)) / (np.max(load) + abs(np.min(load))),
name="shrunk",
)
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(17.5, 5), facecolor=facecolor)
graphic = ax.imshow(load, cmap=remapped_cmap, interpolation="none")
def fa(source=False, use_filter="default", data_file="latest", participant_subset="", drop_metadata=True, drop=[], clean=7, factors=5, facecolor="#ffffff"):
#gets config file:
config = get_config_file(localpath=path.dirname(path.realpath(__file__))+'/')
#IMPORT VARIABLES
if not source:
source = config.get('Source', 'source')
data_path = config.get('Addresses', source)
filter_dir = config.get('Paths', "filter_dir")
filter_name = config.get("Filters", use_filter)
#END IMPORT VARIABLES
filter_path = path.dirname(path.realpath(__file__)) + '/' + filter_dir + filter_name + '.csv'
filters = DataFrame.from_csv(filter_path, header=None).transpose() # transpose filters because of .csv file formatting
all_data = DataFrame.from_csv(data_path + data_file + ".csv")
all_data = all_data.reset_index(level=0)
#~ print filters["metadata"]
#clean data of respondents who only ckeck extreme answers:
all_data = all_data[map(lambda y: len(set(y)) > clean,np.array(all_data))]
if drop_metadata == True:
# drops metadata
all_data = all_data.drop(filters["metadata"][Series.notnull(filters["metadata"])], axis=1)
drop_list = []
for drop_item in drop:
# compile list of column names to be dropped:
drop_list += list(filters[drop_item][Series.notnull(filters[drop_item])])
#get unique column names (the list may contain duplicates if overlaying multiple filters):
drop_list = list(set(drop_list))
all_data = all_data.drop(drop_list, axis=1)
if participant_subset == "odd":
# selects only odd indexes (keep the other dataset half for validation)
keep_rows = all_data.index.values[1::2]
filtered_data = all_data.ix[keep_rows]
elif participant_subset == "even":
# selects only even indexes (keep the other dataset half for validation)
keep_rows = all_data.index.values[0::2]
filtered_data = all_data.ix[keep_rows]
elif participant_subset == "male":
# selects only male participants
filtered_data = all_data[all_data['My legal gender:'] == 'Male']
elif participant_subset == "female":
# selects only female participants
filtered_data = all_data[all_data['My legal gender:'] == 'Female']
else:
filtered_data = all_data
#convert to correct type for analysis:
filtered_data_array = np.array(filtered_data, dtype='float64')
filtered_data_array = filtered_data_array / 100
fit = r.factanal(filtered_data_array, factors, rotation='promax')
load = r.loadings(fit)
load = numpy2ri.ri2numpy(load)
load = r.t(load)
remapped_cmap = remappedColorMap(cm.PiYG, start=(np.max(load)-abs(np.min(load)))/(2*np.max(load)), midpoint=abs(np.min(load))/(np.max(load)+abs(np.min(load))), name='shrunk')
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(17.5, 5), facecolor=facecolor)
graphic = ax.imshow(load, cmap = remapped_cmap, interpolation='none')
ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(base=1.0))
ax.yaxis.set_major_locator(matplotlib.ticker.MultipleLocator(base=1.0))
ax.set_xticklabels([0]+filtered_data.columns.tolist(),fontsize=8,rotation=90)
ax.set_yticklabels(np.arange(factors+1))
ax.set_ylabel('Factors')
ax.set_title("Question Loadings on Factors")
#Recolor plot spines:
for spine_side in ["bottom", "top", "left", "right"]:
ax.spines[spine_side].set_color("#777777")
#Remove ticks:
plt.tick_params(axis='both', which='both', left="off", right="off", bottom='off', top='off')
divider = make_axes_locatable(ax)
#calculate width for cbar so that it is equal to the question column width:
cbar_width = str(100/np.shape(load)[1])+ "%"
cax = divider.append_axes("right", size=cbar_width, pad=0.05)
cbar = colorbar(graphic, cax=cax, drawedges=True)
#Limit the number of ticks:
tick_locator = ticker.MaxNLocator(nbins=6)
cbar.locator = tick_locator
cbar.update_ticks()
#Align ticklabels so that negative values are not misaligned (meaning right align):
for t in cbar.ax.get_yticklabels():
t.set_horizontalalignment('right')
t.set_x(0.045*(np.shape(load)[1]+6))
#Tweak color bar borders
cbar.outline.set_color("#666666")
cbar.dividers.set_linewidth(0)
