def fit(parameters):
# Load the training data
D_train = data.loadData(parameters["training_fasta"])
# Iterate through the fasta file
for fasta in (os.listdir(parameters["k_mers_path"])):
# Get the k-mers of the actual file
K = kmers.loadKmers(parameters["k_mers_path"] + "/" + fasta)
# Generate the samples matrix (X_train) and the target values (y_train)
X_train, y_train = matrix.generateSamplesTargets(
D_train, K, parameters["k"])
# Instantiate a linear svm classifier
clf = SVC(kernel='linear',
C=1,
probability=True,
random_state=0,
cache_size=1000)
# Fit the classifier
clf.fit(X_train, y_train)
# Get index of the separator
index = fasta.index(".")
# Get he filename
file_name = fasta[0:index]
# Save the model
joblib.dump(clf, parameters["model_path"] + "/" + file_name + ".pkl")
# Information message
print("Model: " + file_name + ".pkl saved at: " +
parameters["model_path"])
def identifyPerfectMatch(parameters):
# Display information
print("\nIndentify perfect matches...")
# Initialize the results list
Results = {}
# Get the discriminative motifs
Kmers = kmers.loadKmers(str(parameters["k_mers_path"]))
# Get the sequence dataset
Data = data.loadData(str(parameters["training_fasta"]))
# Add the reference sequence
Data = data.loadReferenceSequence(
Data, str(parameters["refence_sequence_genbank"]))
# Iterate through the k-mers
for kmer in Kmers:
# Display the current motif
print("Signature: " + kmer)
# Get the current k-mer
query = kmer
# Check if there is perfect pairwise alignment of the current kmer with each sequence using parallelization
informations = Parallel(n_jobs=-1)(
delayed(perfectLocalPairwiseSequenceAlignment)(data, query)
for data in Data)
# Save the informations of each sequence according to the current kmer
Results[kmer] = informations
# Return the list of dictionary
return Results
def predict(parameters):
# Get the path of the model file
model_path = str(parameters["model_path"])
# Get the path of the k-mers file
k_mers_path = str(parameters["k_mers_path"])
# Get the testing fasta file
file_path = str(parameters["testing_fasta"])
# Get the prediction file path
prediction_path = str(parameters["prediction_path"])
# Get the evaluation mode
evaluation_mode = str(parameters["evaluation_mode"])
# Load the training data
D = data.loadData(file_path)
# Get the set of k-mers
K = kmers.loadKmers(k_mers_path)
# Get the k-mers length
k = len(list(K.keys())[0])
# Generate the samples matrix (X) and the target values (y)
X, y = matrix.generateSamplesTargets(D, K , k)
# Load the classifier
clf = joblib.load(model_path)
# Predict the sequences
y_pred = clf.predict(X)
# If evaluation mode is egal to True
if evaluation_mode == "True":
# If the target values list is empty
if len(y) == 0: print("Evaluation cannot be performed because target values are not given")
# Else display the classification report
else: print("Classification report \n", classification_report(y, y_pred))
# Save the predictions
f = open(prediction_path, "w")
# Write the header
f.write("id,y_pred\n")
# Iterate through the predictions
for i, y in enumerate(y_pred):
# Save the current prediction
f.write(D[i][0] + "," + y + "\n")
# Close the file
f.close()
# Displays a confirmation message
print("Predictions saved at the path:", prediction_path)
def fit(parameters):
# Get the parameters
model_path = str(parameters["model_path"])
# Get the path of the k-mers file
k_mers_path = str(parameters["k_mers_path"])
# Get the path of the training fasta file
file_path = str(parameters["training_fasta"])
# Load the training data
D = data.loadData(file_path)
# Get the set of k-mers
K = kmers.loadKmers(k_mers_path)
# Get the k-mers length
k = len(list(K.keys())[0])
# Generate the samples matrix (X) and the target values (y)
X, y = matrix.generateSamplesTargets(D, K , k)
# Instantiate a linear svm classifier
clf = svm()
# Fit the classifier
clf.fit(X, y)
# Save the model
joblib.dump(clf, model_path)
# Displays a confirmation message
print("Model saved at the path:", model_path)
def predict(parameters):
# Table of predictions
y_pred = []
# Table of classes
classes = []
# Table of belonging probabilities
probabilities = numpy.empty(0, float)
# Load the testing data
D_test = data.loadData(parameters["testing_fasta"])
# Compute the belonging probability for each model
for fasta, model in zip(os.listdir(parameters["k_mers_path"]),
os.listdir(parameters["model_path"])):
# Get the current model
clf = joblib.load(parameters["model_path"] + "/" + model)
if len(classes) == 0: classes = clf.classes_
# Get the current k-mers
K = kmers.loadKmers(parameters["k_mers_path"] + "/" + fasta)
# Generate the samples matrix (X_test) and the target values (y_test)
X_test, y_test = matrix.generateSamplesTargets(D_test, K,
parameters["k"])
# Load the current model
clf = joblib.load(parameters["model_path"] + "/" + model)
# Compute the membership probabilities for the initial sub-model
if probabilities.shape[0] == 0:
probabilities = clf.predict_proba(X_test)
# Sum the membership probabilities of the additional sub-models
else:
probabilities += clf.predict_proba(X_test)
# Iterate membership probabilities
for p in probabilities:
# Get the maximum score of the array
max_score = numpy.max(p)
# Get the index asocciated to the high score of the array
index = numpy.where(p == max_score)
# Save the prediction
y_pred.append(classes[index][0])
# If evaluation mode is egal to True
if parameters["evaluation_mode"] == "True":
# If the target values list is empty
if len(y_test) == 0:
print(
"Evaluation cannot be performed because target values are not given"
)
# Else display the classification report
else:
print("Classification report \n",
classification_report(y_test, y_pred))
# Save the predictions
f = open(parameters["prediction_path"] + "/prediction.csv", "w")
# Write the header
f.write("id,y_pred\n")
# Iterate through the predictions
for i, y in enumerate(y_pred):
# Save the current prediction
f.write(D_test[i][0] + "," + y + "\n")
# Close the file
f.close()
# Displays a confirmation message
print("Predictions saved at the path:", parameters["prediction_path"])